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Fast segmentation and adaptive SURF descriptor for iris recognition
Mathematical and Computer Modelling (
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Mathematical and Computer Modelling journal homepage: www.elsevier.com/locate/mcm
Fast segmentation and adaptive SURF descriptor for iris recognition Hunny Mehrotra ∗ , Pankaj K. Sa, Banshidhar Majhi Department of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India
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Article history: Received 8 March 2011 Received in revised form 1 March 2012 Accepted 30 June 2012 Keywords: Adaptive thresholding Hole filling Adaptive normalization Gamma enhancement SURF Iris biometrics
abstract In this paper a robust segmentation and an adaptive SURF descriptor are proposed for iris recognition. Conventional recognition systems extract global features from the iris. However, global features are subject to change for transformation, occlusion and nonuniform illumination. The proposed iris recognition system handles these issues. The input iris image is used to remove specular highlights using an adaptive threshold. Further, the pupil and iris boundaries are localized using a spectrum image based approach. The annular region between the pupil and iris boundaries is transformed into an adaptive strip. The strip is enhanced using a gamma correction approach. Features are extracted from the adaptive strip using Speeded Up Robust Features (SURF). The results obtained using SURF are compared with the existing SIFT descriptor and the proposed approach performs with improved accuracy and reduced computation cost. © 2012 Elsevier Ltd. All rights reserved.
1. Introduction With the present need of trust for various activities like financial transactions, access to networks and information, there is a strong requirement to develop an authentication system. Iris biometrics plays an important role in providing a promising solution for authentication of an individual using unique texture patterns. The iris is an internal organ whose texture remains stable throughout the life of an individual. The randomness of iris patterns is used to distinguish between individuals. A generic iris biometric system performs localization of the pupil and iris boundaries, generates iris codes and matches the two iris templates to perform authentication. Traditional iris biometric systems assume that the input is an ideal iris image without occlusion or severe transformations. However, the problem is aggravated when noise comes into consideration due to occlusion, non-uniform illumination, change in viewpoint of an individual or head tilt during acquisition. Iris recognition using such noisy images is a challenging problem. There has been considerable research into iris recognition using ideal images. Daugman has proposed an automated iris recognition system in [1]. Existing works on iris recognition mainly focus on analyzing the texture using different approaches. These approaches for recognition can be broadly classified into four categories.
• Texture based method: The texture analysis of local spatial patterns is used to extract features. In [2] a Laplacian of Gaussian filter at multiple scales is used for matching two irises. In [3] the radially smoothed iris image is decomposed in the angular direction using a one dimensional continuous wavelet transform. Iris textures are analyzed in [4] to capture the discriminating frequency information. Specific filters with distinct center frequencies are applied to three different portions to extract the texture features of the iris. Different weights are given to each portion depending on its contribution to recognition. An efficient iris recognition algorithm, obtained through the fusion of the Haar wavelet and the circular Mellin operator has been proposed in [5]. The approach proposed in [6] uses a bank of Gabor filters to capture iris characteristics.
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Corresponding author. E-mail address: [email protected] (H. Mehrotra).
0895-7177/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.mcm.2012.06.034
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Fig. 1. Block diagram of proposed iris recognition system.
• Phase based method: In this type of method the phase angles are assigned to the iris pattern by finding the sub-pixel image translation. A Gaussian filter at multiple scales is used to produce a template and computes the normalized correlation as a similarity measure [1]. A phase-based image matching technique for iris recognition using 2D discrete Fourier transforms (DFTs) is given in [7]. • Zero crossing based method: Zero-crossings of the wavelet transform at various resolution levels are calculated over concentric circles on the iris, and the resulting one-dimensional (1D) signals are compared with model features using different dissimilarity functions [8]. • Keypoint descriptors: Local features are based on the appearance of an object at particular interest points and are invariant to image scale and rotation. They are also robust to changes in illumination, noise, and minor changes in viewpoint. This technique of local feature extraction is applied to the iris using a region based SIFT approach [9]. The idea is to develop a keypoint descriptor that is capable of performing well for iris textures. Further, the most significant local extrema of the first two Taylor expansion coefficients as descriptors of the iris texture are proposed in [10]. In [11], a dual stage corner point descriptor is proposed that uses spatial location and entropy information of a window around the corner. In [12] the authors used, another keypoint descriptor, Speeded Up Robust Features (SURF), to perform recognition directly from annular iris images. In [13], the Gabor wavelet is combined with SIFT for feature extraction and the system is tested using frontal and off-angle iris images. The texture based approaches perform well for constrained databases with partial occlusion, uniform illumination and high quality input. However, the texture pattern exhibits significant change in characteristics for variation in scale and illumination. Thus, there is a need to develop a feature extractor that is invariant to various possible transformations and occlusion. Keypoint descriptors are well known approaches for object recognition as proposed by Lowe [14]. To characterize the features in the spatial domain, interest points known as keypoints are detected. A descriptor vector is formed around every detected keypoint to extract features invariant to several transformations. Due to their inherent advantages, local descriptors could find applicability to iris recognition. The proposed iris recognition system provides an improvement over existing approaches at various phases. (i) During preprocessing, a phase adaptive threshold is determined to generate a binary image. The specular highlights lying on the pupil region are detected and removed using a morphological hole filling operation. (ii) During iris normalization, an adaptive strip is generated to overcome sampling artifacts unlike the fixed sized strip as in earlier proposed works. (iii) During enhancement, the adaptive strip is enhanced to reduce the effect of non-uniform illumination. (iv) During feature extraction, SURF features are extracted from an adaptive strip that possesses invariance to various possible transformations and partial occlusion. A block diagram of the proposed approach is shown in Fig. 1. The paper is organized as follows. The preprocessing and enhancement of the iris image are explained in detail in Section 2. From the acquired iris image the pupil and iris boundaries are segmented as given in Section 2.1. The localized image is used to normalize the iris into an adaptive strip to overcome sampling error as given in Section 2.2. Further, the variable sized strip is enhanced to achieve invariance to illumination as given in Section 2.3. The features are extracted from the enhanced adaptive strip using Speeded Up Robust Features (SURF) [15]. The results for the proposed iris recognition system are given in Section 5 and compared with the existing SIFT approach. Finally, the conclusions are given in Section 6. 2. Preprocessing and enhancement In this paper a faster segmentation approach is proposed that performs more accurately in comparison to traditional segmentation approaches. The acquired iris image contains redundant information that should be detected and removed
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Fig. 2. The relationship between τ and η.
to extract the unique features from the iris. During the preprocessing phase, the input iris image is used to find the pupil and iris boundaries. The annular region lying between the two boundaries is normalized into an adaptive sized rectangular block. Finally, the rectangular block is enhanced for feature extraction. A detailed description of the steps involved are given as follows. 2.1. Iris segmentation In this approach iris segmentation is performed using a non-parametric spectrum image approach. The input iris image is binarized using an adaptive threshold. Further, the pupil boundary is localized using a spectrum image approach and the iris circle is delineated using circular summation of intensities. The annular region between the pupil and iris boundaries is transformed into a rectangular block of variable size to overcome aliasing artifacts. This block is adaptive to the size of the iris that varies due to illumination. 2.1.1. Adaptive thresholding The pupil is the darkest region in the eye with almost circular shape. An appropriate threshold helps to determine the region of interest due to the pupil. A static value of the threshold may fail for different images taken under varying illumination conditions [16]. In this paper an effort has been made to adaptively determine the value of the threshold. It has been empirically observed that the highest intensity value contributing to the pupil neither exceeds highτ (0.5 times the highest grayscale value) nor drops beyond lowτ (0.1 times the highest grayscale value). To find the adaptive threshold, binary images are obtained iteratively for a range of thresholds (τ ) between lowτ and highτ with increments of stepτ (0.05 times the highest grayscale value). The parameters are optimized based on a trade off between computational complexity and accuracy. The binary images obtained for varying τ are considered to remove specular highlights (holes). A morphological region filling approach is used to fill holes in the image. To begin the hole filling operation, the binary image (A) is complemented. The convention adopted here is that the boundary pixels are labelled as 1. If non-boundary pixels are labelled as 0 then beginning with a point p inside the boundary a value of 1 is assigned. The following transformation fills the region with ones: Xk = (Xk−1 ⊕ S ) ∩ Ac
(1)
where X0 = p; k = 1, 2, 3, . . .; ⊕ is used for dilation of Xk−1 by S which is defined as Xk−1 ⊕ S = {z |(Sˆ )z ∩ Xk−1 ̸= φ}.
(2)
S is the symmetric structuring element defined as
0 1 0
1 1 1
0 1 . 0
This algorithm terminates at the kth iteration if Xk = Xk−1 . The image generated from the last iteration Xk is combined with A using a bitwise OR that contains the boundary filled image. A graphical representation of the hole filling process is shown in Fig. 3. Each hole filled image is used to find the number of connected components (η). η changes for a change in value of the threshold as shown in Fig. 2. The value of threshold corresponding to minimum non-zero η is chosen as the adaptive
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Algorithm 1 Adaptive_Thresholding Require: I: Intensity Image, S: Structuring element Ensure: B: Binary Image lowτ ⇐ 0.10 highτ ⇐ 0.50 stepτ ⇐ 0.05 [r c ] := size(I) {Compute width and height of image} for τ := lowτ to highτ step stepτ do A := binary(I , τ ) {Image Binarization using τ } C := Ac {Complement of an image} X0 := zeros(r , c ) {Image with all zeros} X0 (p) = 1 {p is a point inside hole} k⇐0 repeat k⇐k+1 Xk ⇐ (Xk−1 ⊕ S ) ∩ C until Xk ̸= Xk−1 Hτ ⇐ Xk ∪ A {Hole filled image} ητ := connComp(Hτ ) {Find no. of connected components} end for pos := min_nonzero(η){Find index of minimum non-zero} B ⇐ Hpos
Fig. 3. A: block of the binary image with holes, Ac : complement of A, X0 : image with the first pixel in the boundary, X1 : image after first iteration, Xk : image after kth iteration, H: hole filled image, B: structuring element.
binarization threshold. However, if the minimum non-zero η occurs for more than one threshold (as shown in Fig. 2), then the maximum threshold amongst them is chosen as the adaptive threshold. The reason behind finding the maximum amongst potential thresholds is that the pupil boundary may contain some intensity values which may not contribute to the connected component of the pupil for lower thresholds. Fig. 4 shows binary images obtained for change in τ . Algorithm 1 describes the steps involved. 2.1.2. Pupil detection The traditional segmentation approach [17] finds the pupil and iris circles using the circular Hough transform [18]. The major drawback of the Hough transform is that it requires a range of radius as input from the user and performs localization in R3 parameter space (number of parameters required to describe the shape of a circle) which implies high time complexity of the transform. In this paper a faster approach is used to find the pupil circle without any pre-estimation of the radius range as input. The proposed approach is non-parametric in the sense that it does not require any prior approximation of the circle parameters. The binary image is re-complemented to form the spectrum image [19]. The distance of every pixel in the binary image is obtained with nearest non-zero pixel. By computing the distance between non-zero pixels, the spectrum showing the largest filled circle can be formed within the set of foreground pixels. Since the pupil is the largest filled circle in the image the overall intensity of this spectrum peaks in the center. The spectrum image is shown in Fig. 5(a). Thus, the position
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(a) τ = 0.10; η: 0.
(b) τ = 0.15; η: 0.
(f) τ = 0.35; η: 23.
(c) τ = 0.20; η: 1.
(g) τ = 0.40; η: 18.
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(d) τ = 0.25; η: 1.
(h) τ = 0.45; η: 23.
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(e) τ = 0.30; η: 32.
(i) τ = 0.50; η: 30.
Fig. 4. Binary images obtained for change in threshold (τ ) and number of connected components (η).
a
b
c
Fig. 5. Pupil detection. (a) Spectrum image. (b) Edge detected image with pupil center. (c) Pupil localized image.
of the maximum value in the spectrum image is the pupil center. To compute the pupil radius, an edge map of the hole filled binary image is obtained as shown in Fig. 5(b). In the edge map, the distance from the detected pupil center to the nearest non-zero pixel is the pupil radius. The pupil detected image is shown in Fig. 5(c). The algorithm for detecting the pupil center and radius is given as Algorithm 2.
Algorithm 2 Pupil_Detection Require: B: Binary Image Ensure: xc : xcenter of pupil, yc : ycenter of pupil, rp : Radius of pupil C ⇐ Bc {Complement the binary image} [x y] := find(C == 1) {Find location of ones in an image} l := length (x) {To find the no. of elements in an array} for i := 1 to r do for j := 1 to c do for k := 1 to l do Dk ⇐ (xk − i)2 + (yk − j)2 end for Si,j := min(D) {Minimum value of D} end for end for [xc yc ] ⇐ max(S ) E := edge(C ) {Edge detection} j ⇐ yc {Estimation of pupil radius} rp ⇐ 0 while Exc ,j ̸= 1 do rp ⇐ rp + 1 j⇐j+1 end while
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Fig. 6. Iris detection. (a) Contrast enhanced image. (b) Concentric circles of different radii. (c) Iris localized image.
2.1.3. Iris detection For iris detection the input iris image is blurred to remove external noise. But too much blurring may make it difficult to detect the outer iris boundary, separating the eyeball and sclera. Thus, a special smoothing filter such as a median filter is used on the original intensity image. This type of filtering eliminates sparse noise while preserving image boundaries [20]. After filtering, the contrast of the image is enhanced to have sharp variation at the image boundaries using histogram equalization as shown in Fig. 6(a). This contrast enhanced image is used for finding the outer iris boundary by drawing concentric circles (Fig. 6(b) shows an example) of different radii from the pupil center and the intensities lying over the perimeter of the circle are summed up. Among the candidate iris circles, the circle having maximum change in intensity with respect to the previously drawn circle is the iris outer boundary as shown in Fig. 6(c). The algorithm for detection of the iris radius is given as Algorithm 3. Algorithm 3 Iris_Detect Require: I: Input image, rp : radius of pupil, xc : xcenter of pupil, yc : ycenter of pupil Ensure: ri: radius of iris F ⇐ medianFilt(I){Median Filtering on input image} H ⇐ Histeq(F) {Histogram equalization} [r c ] ⇐ size(I) {Finding image dimensions} {Finding the intensity over circumference} for ri = rp × 1.5 to 2r do sumri ⇐ 0 for θ = 0 to 360 do x = xc + ri × cos(θ ) y = yc + ri × sin(θ ) sumri = sumri + Hx,y end for ri = ri + 2 end for {Change in intensity over circumference} for i = 1 to ri do Di = |sumi − sumi+1 | end for [d ri] = max(D) {Maximum change in intensity}
2.2. Adaptive normalization In texture recognition approaches the extracted features should be invariant to change in scale, position and orientation. Thus the annular region between the pupil and iris boundaries is normalized to achieve the aforementioned invariance. There exists a traditional approach to iris normalization [1] that transforms the Cartesian coordinates to a doubly dimensionless polar coordinate system. The main objective is to maintain reference to the same region of iris texture irrespective of camera to eye distance and expansion/dilation of the pupil. The rubber sheet model as proposed by Daugman [1] assigns to each point in the iris regardless of size and pupillary dilation a pair of dimensionless real coordinates. However, the size of the pupil is controlled by muscles in the iris which control the amount of light entering the eye. Thus the pupil shrinks in bright light and expands in low light. The variable size of the pupil causes linear deformation of the texture information and creates an aliasing effect. Further, the change in distance between the camera lens and the user’s eye scales the features. In addition to this, during acquisition there may be a slight tilt in the user’s head. This rotates features in a
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Fig. 7. The effect of aliasing on iris normalization using fixed and adaptive strips.
circular direction. Normalizing the distance between pupil and iris boundary creates an aliasing effect in the iris texture [21]. The conversion into dimensionless coordinates introduces linear deformation of the texture. Thus, transformation scales the features and deteriorates the performance of the system. Fig. 7 shows two instances of the same eye taken from the CASIAV3 database under varying illumination conditions. The strips generated using traditional approaches are of fixed size (100×360 pixels) as given in Fig. 7(a.1) and (b.1) respectively. From the images it is evident that although the region lying between the pupil and iris boundaries is not uniform in both the images, the texture features are scaled to some constant size to render a scale invariant image. This can be regarded as sampling the data with the possibility of aliasing [21]. To overcome aliasing artifacts, the proposed scale based approach normalizes the iris image by converting it from Cartesian space to non-uniform (dimension dependent) polar space. The points lying on the perimeter of the iris and pupil circle are obtained using xp (θ ) = xc + rp × cos(θ ) yp (θ ) = yc + rp × sin(θ )
(3)
xi (θ ) = xc + ri × cos(θ ) yi (θ ) = yc + ri × sin(θ ) where xp (θ ) and yp (θ ) are the points lying on the pupil circle, xi (θ ) and yi (θ ) are the points lying on the iris circle, while rp and ri are the pupil and iris radius respectively. The variant to normalization is achieved by taking the range of radius between the pupil and iris boundaries and mapping the points to a rectangle using the actual distance (ri − rp ). The mapping equations are given as follows: x(ρ, θ ) = (1 − ρ)xp (θ ) + ρ × xi (θ ) y(ρ, θ ) = (1 − ρ)yp (θ ) + ρ × yi (θ )
(4)
where ρ is given in the interval of [0 : ρinc : 1] and ρinc is defined by
ρinc =
1 ri − rp
.
(5)
The images given in Fig. 7(a.2) and (b.2) are generated using the adaptive normalization approach. Here the strip is formed depending upon the actual distance between the inner and outer boundaries. Thus, the features do not undergo any deformation due to scaling. The algorithm for iris normalization using the proposed approach is given as Algorithm 4. 2.3. Strip enhancement Enhancement is carried out to overcome the effect of non-uniform illumination, shadowing and highlights in the strip. The objective is to remove noise while still preserving essential texture information. The process starts with gamma correction that is defined by
IE =
Iγ log(I )
if γ ≥ 0 if γ = 0
(6)
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Algorithm 4 Adaptive_Normalization Require: I: Input image, rp : radius of pupil, ri : radius of iris, xc : xcenter of pupil, yc : ycenter of pupil Ensure: N: Normalized Image for θ = 0 to 360 do xp (θ ) = xc + rp × cos(θ ) yp (θ ) = yc + rp × sin(θ ) xi (θ ) = xc + ri × cos(θ ) yi (θ ) = yc + ri × sin(θ ) end for
ρinc = ri −1rp for ρ = 0 : ρinc : 1 do for θ = 0 to 360 do x = (1 − ρ) × xp (θ ) + ρ × xi (θ ) y = (1 − ρ) × yp (θ ) + ρ × yi (θ ) N (ρ, θ ) = I (x, y) end for end for
where γ ∈ [0 : 1], I is the normalized iris image and IE is the image after gamma correction as shown in Fig. 8(b). Gamma correction has a characteristic of enhancing local dark regions while compressing bright regions and highlights [22]. However, Gamma correction does not remove the shading effects caused by eyelashes and eyelids. High pass filtering removes useful as well as incidental information. Similarly, suppressing the highest spatial frequencies reduces aliasing. Difference of Gaussian (DOG) filtering is used to achieve these effects. Given an enhanced input image IE , the Gaussian blurring is obtained by L(x, y, σ ) = IE (x, y) ∗ G(x, y, σ )
(7)
where G(x, y, σ ) =
1 2π σ
2
exp
−(x2 + y2 ) . σ2
(8)
The result of convolving an image with a difference of Gaussian filter is given by D(x, y) = L(x, y, σ1 ) − L(x, y, σ2 )
(9)
where σ1 is the outer Gaussian and contains more pixels (≈2–4 pixels) and σ2 is the inner Gaussian and is quite narrow (one pixel). The filtered image is shown in Fig. 8(c). Further, after DOG filtering the intensity values are rescaled to standardize the intensity variation and discard some extreme values as given in [22]. A simple approximation using a two stage process is given by I ( x, y ) = I ( x, y ) =
D(x, y)
(10)
1
(mean(|I (x, y)|a )) a I (x, y)
1
(mean(min(τ , |I (x, y)|)a )) a
(11)
where a is an exponent that reduces large values and τ is the threshold which is used to truncate large values after transformation as given in Eq. (10). The resultant image is well scaled but may still contain some extreme values, thus a hyperbolic tangent is used to discard these values as given by I (x, y) = τ tanh
I ( x, y )
τ
.
(12)
The contrast adjusted image obtained from Eq. (12) is in the range [−τ : τ ]. This image is further rescaled to be in the range [0 : 1] using min–max normalization. The contrast adjusted image is shown in Fig. 8(d). Further histogram equalization is applied on the resultant image to get the enhanced image as shown in Fig. 8(e). 3. Speeded up robust features for the adaptive strip The iris strip is categorized as repeated occurrence of texture elements known as texels. Texels in an image are statistically similar but not identical. As proposed in Section 2.2, the normalized iris image is of variable size hence it is not well suited for matching using global transforms like DCT [23], Haar [8], gabor wavelets [1], etc. Further, the relative positions of texels change and cannot be mapped to the same locations in polar coordinates without some transformation [9]. Thus,
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(a) Input image.
(b) Gamma corrected.
(c) DOG filter.
(d) Contrast equalisation.
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(e) Histogram equalisation. Fig. 8. Intermediate images obtained during enhancement of the iris strip.
some local feature extraction technique is required that is invariant to change in scale, rotation, occlusion and viewpoint of two iris images. The local features around special points known as keypoints/keytexels are obtained and compared to find the similarity between the images. The most valuable property of a keypoint detector is its repeatability, i.e. whether it reliably finds the same interest points under different viewing conditions [15]. To extract features around keytexels the neighborhood of every detected point is represented by a feature vector (descriptor). This descriptor has to be distinctive and at the same time robust to transformations, illumination and partial occlusions. A considerable amount of work has been carried out for local feature detection. The most widely used interest point detector is the Harris corner detector, based on eigenvalues of the second moment matrix [24]. The Harris corner detector is applied to the iris to extract corner points and the entropy information of a window around the corner is taken as the descriptor [11]. However, Harris corners are not scale invariant and fail to achieve the property of repeatability when the iris strip scales due to illumination or change in the camera to eye distance. Thus, the technique is found to be unsuitable for scale based normalization. Further, an adaptive scale based Harris measure has been proposed in [25] where the determinant of a Hessian matrix is used to select the location, and a Laplacian to select the scale. A detailed study of detectors has been made in [15] and it has been concluded that Hessian-based detectors are more stable and repeatable than Harris-based detectors. However, the location of interest points as features does not yield satisfactory results because it has been inferred that the locations may undergo change due to transformations. Thus, a feature descriptor termed the Scale Invariant Feature Transform (SIFT) has been proposed that stores a substantial amount of information around the interest points [14] using an orientation histogram. SIFT features have proved to perform with high accuracy along with low computation time. Region based SIFT has been proposed for annular iris images [9]. The system performs well for non-cooperative iris databases as well. In this paper, the most promising approach, Speeded Up Robust Features (SURF) [15], is used which performs better compared to SIFT with low computational cost. SURF uses the same matching approach as SIFT but with a few variations. Firstly, SURF uses the sign of the Laplacian to have a sharp distinction between background and foreground features. Secondly, SURF uses only 64 dimensions compared to SIFT using a 128 dimensional vector. This reduces feature computation time and allows quick matching with increased robustness simultaneously [26]. The SURF operator extracts keytexels using a Hessian matrix and describes a distribution of Haar wavelet responses from a window around the interest point as descriptors. These features are found to be very distinctive and stable. SURF is found to be far more stable and unique compared to state of the art approaches. SURF when applied to an adaptive strip is termed an adaptive SURF descriptor. A detailed description of iris feature extraction using SURF is given as follows. 3.1. Keytexel detector For detection of keytexels the determinant of a Hessian matrix is used for selecting location and scale. Given a point P = (x, y) in an image I, the Hessian matrix H (P , σ ) in P at scale σ is defined as follows: L (P , σ ) H (P , σ ) = xx Lxy (P , σ )
Lxy (P , σ ) Lyy (P , σ )
where Lxx (P , σ ) is the convolution of the Gaussian second order derivative
(13)
σ2 g (σ ) σ x2
with the image I at the point P and
Lxy (P , σ ) and Lyy (P , σ ) are obtained similarly. The discrete Gaussian derivatives and their approximations for a 9 × 9 box
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Fig. 9. Detected texel points on an adaptive strip for two instances of the same individual taken from the CASIAV3 database.
filter at σ = 1.2 are denoted by Dxx , Dxy and Dyy [27]. Image convolutions with the box filters are computed rapidly using integral images [28]. The integral image at location (x, y) is defined as sum of all pixels above and left of (x, y) Is (x, y) =
y x
I (i, j)
(14)
i =0 j =0
where Is is the integral image for the input image I. By choosing the weights for the box filters adequately, the approximations for the Hessian’s determinant are computed using Det(Happrox ) = Dxx Dyy − (0.9Dxy )2 .
(15)
Keytexels are localized in scale and image space by applying a non maximum suppression in a 3 × 3 × 3 neighborhood. The local maxima found on the determinant of the Hessian matrix are interpolated to image space. 3.2. Keytexel descriptor A circular window is constructed around every detected texel point and orientation is estimated using Haar wavelet responses in both horizontal and vertical directions. The orientation helps to have invariance to rotation. Further, SURF descriptors are obtained by taking a rectangular window around every detected keytexel in the direction of orientation obtained earlier. The windows are split into 4 × 4 sub-regions to take into consideration the spatial information. In each subregion Haar wavelet responses extracted in the horizontal and vertical directions (dx and dy ) are summed up. The wavelet responses are summed up along with the absolute values to find the polarity of image intensity changes. The feature vector of each sub-image is given by V =
dx ,
dy ,
|dx |,
|dy | .
(16)
Thus, by summing up the descriptor vectors from all 4 × 4 sub-regions, a feature descriptor of length 64 is obtained. The descriptor vector of length 64 for each keytexel is known as the keytexel descriptor. Fig. 9 shows the number of keytexels detected using two different instances of the same individual from the CASIAV3 database [29]. The number of detected texel points varies with change in the size of the strip. This highlights the effect of illumination on the feature extractor. The features may undergo transformation due to rotation, scaling and occlusion due to eyelids. Thus, there is a need to use a feature descriptor that posses invariance to the aforementioned issues. SURF is one such very reliable keypoint descriptor that has been applied to iris recognition for better accuracy. 4. Keypoint pairing After detection of keytexels in database image (A) and query image (B), matching is carried out using a point pairing approach. The best candidate match for each texel point in A is found by identifying the closest pair from the set of texel points in B. The nearest neighbor is defined as the texel point with minimum Euclidean distance for the invariant descriptor vector. Let L = {l1 , l2 , l3 , . . . , lm } and E = {e1 , e2 , e3 , . . . , en } be vector arrays of keytexels of A and B respectively obtained through SURF. The descriptor array li of texel point i in L and descriptor array ej of texel point j in E are paired if the Euclidean distance ∥li − ej ∥ between them is less than a specified threshold ψ . Threshold based pairing results in several matching points. To avoid multiple matches, the keytexels with minimum descriptor distance and less than the threshold are paired. This results in a single matching pair, and is called the nearest neighborhood matching method. In SURF, the matching method applied
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Table 1 Mis-localization percentages of Masek’s approach and the proposed approach. Database
Masek
Proposed
BATH CASIAV3 UBIRIS
37.62 05.23 10.53
0.98 0.45 3.41
Table 2 Average localization time taken (in seconds) for the proposed approach and Masek’s approach. Approach
Masek [17] Proposed
Databases BATH
CASIAV3
UBIRIS
02.2820 00.3383
13.0676 00.3960
01.7656 00.2820
is similar to nearest neighbor matching, except that the thresholding is applied to the descriptor distance ratio between keytexels. The method used in SURF is called the nearest neighbor ratio method. Thus, the texel points are matched if
∥li − ej ∥/∥li − ek ∥ < ψ
(17)
where ej is the first nearest neighbor and ek is the second nearest neighbor of li . The paired points (li , ej ) are removed from L and E respectively. The matching process is continued until there are no more key texels. Based on the number of pairs between the live image B and enrolled image A, a decision about the candidate’s identity is taken. 5. Experimental results To measure the performance of the iris recognition system experimental results are obtained on various available datasets such as BATH [30], CASIA-IrisV3 (CASIAV3) [29] and UBIRIS [31]. The database available from Bath University comprises images from 50 subjects. For each subject both left and right iris images are obtained, each containing 20 images of the respective eyes. CASIAV3 is acquired in an indoor environment. Most of the images are captured in two sessions, with at least one month interval. The database contains 249 subjects with a total of 2655 images from left and right eyes. The UBIRIS.v1 database is composed of 1877 images collected from 241 persons in two distinct sessions [31]. The main characteristics of the database are that it incorporates several noise factors to measure the performance of iris recognition systems. The results are obtained for the proposed segmentation and novel feature descriptor in two different stages. 5.1. Segmentation performance At the first level, the performance of the proposed localization approach is compared with Masek’s benchmark approach [17]. Masek’s approach uses a circular Hough transform for detection of the pupil and iris boundaries. Table 1 gives the mis-localization percentage by the proposed approach and Masek’s approach. The proposed approach gives a mis-localization of 3.41% on the UBIRIS noisy iris database with significantly low mis-localizations of 0.98% and 0.45% on the BATH and CASIAV3 databases. Masek’s approach performs with considerably high mis-localization percentage for the BATH database and noisy UBIRIS database. Fig. 10 shows localized iris images using Masek’s approach and the proposed approach. Masek’s approach localizes the inner pupil boundary accurately for most samples from the CASIAV3 database but fails to localize the outer iris boundary. However, errors due to localization increase for the noisy and low resolution BATH and UBIRIS databases. Thus, Masek’s approach fails for low resolution images whereas the proposed approach possesses invariance to rotation, viewpoint and quality of the input image. The average times taken in seconds/image using the proposed approach and Masek’s approach are given in Table 2. The results are generated using an AMD Athlon dual core processor with 2.81 GHz processor speed and 2.00 GB of RAM. The experimental results show that the proposed approach performs considerably faster compared to Masek’s approach. The Hough transform performs circle fitting for radius range, which is quite expensive for the high resolution images from the CASIAV3 database. The proposed approach finds the circle parameters in considerably less time with better accuracy. The segmentation approach performs localization in 00.3960 s/image in comparison to Masek’s approach which performs localization in 13.0676 s/image. 5.2. Recognition performance At the second level of the experiment the accuracy results are obtained using the proposed segmentation approach. The adaptive strip is enhanced and features are extracted using SURF and SIFT. Table 3 shows the accuracy values obtained on the adaptive strip after enhancement. SIFT performs poorly with an accuracy of 77% on the noisy UBIRIS database whereas the accuracy improves significantly to 96.91% using the adaptive strip. The accuracy improves from 85.69% for a fixed strip
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Fig. 10. Localization results using Masek’s approach and the proposed approach.
Table 3 Results generated using SIFT and SURF on fixed and adaptive strips. Approach
Database UBIRIS
SIFT SURF
Fixed strip Adaptive strip Fixed strip Adaptive strip
BATH
CASIAV3
FAR
FRR
Accuracy
FAR
FRR
Accuracy
FAR
FRR
Accuracy
13.08 6.16 7.34 5.02
32.91 0.00 4.11 1.80
77.00 96.91 94.27 96.58
8.01 4.56 7.98 1.44
10.37 5.54 3.46 2.06
90.80 94.94 94.27 98.24
7.16 3.74 3.39 1.55
19.64 4.34 4.78 3.80
86.59 95.95 95.91 97.32
Table 4 Average recognition time taken (in seconds) for fixed and adaptive strips. Test cases
Databases BATH
SIFT SURF
CASIAV3
UBIRIS
Fixed
Adaptive
Fixed
Adaptive
Fixed
Adaptive
0.977 0.156
0.543 0.053
0.911 0.267
0.899 0.154
0.563 0.282
0.368 0.043
to 95.95% for an adaptive strip using SIFT on the CASIAV3 database. The system is marginally acceptable for the constrained images in the BATH database. Further, SURF is applied on fixed and adaptive strips of irises and results are obtained. The system performs with an accuracy of 94.27% using a fixed strip on the UBIRIS database. The accuracy increases to 96.58% after taking an adaptive size of strip. Similar observations are made for the CASIAV3 database. The gain in accuracy is significant for the constrained BATH iris database. The average times taken in seconds using SIFT and SURF are given in Table 4. The SIFT feature descriptor takes 0.977 s/image for the BATH database using a fixed strip. The time reduces to 0.543 s/image after using an adaptive strip. Similar results are computed for the CASIAV3 and UBIRIS databases. However, the time required to perform recognition using SURF is extremely low for an adaptive strip on the various available databases. The receiver operating characteristic (ROC) is the plot of false acceptance rate (FAR) versus genuine acceptance rate (GAR). A plot of ROC is obtained for SIFT on the various available databases as shown in the left column of Fig. 11. Similarly, an ROC curve is obtained for SURF as shown in the right column of Fig. 11. From the results it is observed that the adaptive SURF descriptor performs with higher accuracy in less time. The distributions of genuine and imposter scores for adaptive SIFT and adaptive SURF are shown in Fig. 12. 6. Conclusion In this paper an effort has been made to develop a robust iris recognition system that possesses invariance to various possible transformations, occlusion and illumination. The proposed segmentation approach is compared to the benchmark
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Fig. 11. ROC curve for SIFT and SURF using fixed and adaptive strips.
localization approach used by Masek [17]. The proposed approach performs faster with better accuracy. Further, during normalization the iris is normalized to generate an adaptive strip for feature extraction. This adaptive strip is enhanced to overcome the effect of non-uniform illumination. For feature extraction, a well known keypoint descriptor named SURF is used. The adaptive iris recognition system performs with an accuracy of 96.58% on the noisy UBIRIS database and with
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Fig. 12. Distribution of genuine and imposter scores for the adaptive strip.
accuracies of 98.24% and 97.32% on the BATH and CASIAV3 databases. The time required to claim identification using SURF is comparatively low compared to SIFT. From the results it is found that the proposed system performs faster with improved accuracy. Thus, the system can be used for highly trust based applications such as financial transactions and security.
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References [1] J. Daugman, High confidence visual recognition of persons by a test of statistical independence, IEEE Transactions on Pattern Analysis and Machine Intelligence 15 (11) (1993) 1148–1161. [2] R. Wildes, Iris recognition: an emerging biometric technology, Proceedings IEEE 85 (9) (1997) 1348–1363. [3] J. Kim, S. Cho, J. Choi, R. Marks II, Iris recognition using wavelet features, Journal of VLSI Signal Processing Systems 38 (2) (2004) 147–156. [4] R. Ng, Y. Tay, K. Mok, Iris recognition algorithms based on texture analysis, in: International Symposium on Information Technology, vol. 2, 2008, pp. 1–5. [5] H. Mehrotra, P. Gupta, A. Kaushik, Texture based iris recognition system, in: Independent Component Analyses, Wavelets, Unsupervised NanoBiomimetic Sensors, and Neural Networks VI, in: SPIE, vol. 6979, Orlando, FL, USA, 2008, p. 697903. [6] L. Ma, Y. Wang, T. Tan, Iris recognition based on multichannel gabor filtering, in: Proceedings of Fifth Asian Conference on Computer Vision, vol. 1, 2008, pp. 279–283. [7] K. Miyazawa, K. Ito, T. Aoki, K. Kobayashi, H. Nakajima, An efficient iris recognition algorithm using phase-based image matching, in: Proceedings of IEEE International Conference on Image Processing, vol. 2, 2005, pp. 49–52. [8] W. Boles, B. Boashash, A human identification technique using images of the iris and wavelet transform, IEEE Transactions on Signal Processing 46 (4) (1998) 1185–1188. [9] C. Belcher, Y. Du, Region-based SIFT approach to iris recognition, Optics and Lasers in Engineering 47 (1) (2009) 139–147. [10] A. Bastys, J. Kranauskas, R. Masiulis, Iris recognition by local extremum points of multiscale Taylor expansion, Pattern Recognition 42 (9) (2009) 1869–1877. [11] H. Mehrotra, G. Badrinath, B. Majhi, P. Gupta, An efficient dual stage approach for iris feature extraction using interest point pairing, in: IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, 2009, pp. 59–62. [12] H. Mehrotra, B. Majhi, P. Gupta, Annular iris recognition using SURF, in: Pattern Recognition and Machine Intelligence, in: Lecture Notes in Computer Science, vol. 5909, Springer, 2009, pp. 464–469. [13] Y. Du, C. Belcher, Z. Zhou, Scale invariant gabor descriptor-based noncooperative iris recognition, EURASIP Journal on Advances in Signal Processing 2010 (2010) 37:1–37:13. [14] D. Lowe, Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision 60 (2) (2004) 91–110. [15] H. Bay, A. Ess, T. Tuytelaars, L.V. Gool, SURF: speeded up robust features, Computer Vision and Image Understanding (CVIU) 110 (3) (2008) 346–359. [16] G. Xu, Z. Zhang, Y. Ma, Automatic iris segmentation based on local areas, in: International Conference on Pattern Recognition, vol. 4, IEEE Computer Society, 2006, pp. 505–508. [17] L. Masek, P. Kovesi, Matlab source code for a biometric identification system based on iris patterns, The School of Computer Science and Software Engineering, The University of Western Australia, 2003. [18] C. Kimme, D. Ballard, J. Sklansky, Finding circles by an array of accumulators, Communications of the ACM 18 (2) (1975) 120–122. [19] B. Lipinski, Iris recognition: detecting the pupil, in: Connexions Web Site, 2004. http://cnx.org/content/m12487/1.4/. [20] R. Gonzalez, R. Woods, Digital Image Processing, third ed., Prentice Hall, 2007. [21] H. Proenca, L. Alexandre, Iris recognition: an analysis of the aliasing problem in the iris normalization stage, in: International Conference on Computational Intelligence and Security, vol. 2, 2006, pp. 1771–1774. [22] X. Tan, B. Triggs, Enhanced local texture feature sets for face recognition under difficult lighting conditions, in: Analysis and Modelling of Faces and Gestures, in: LNCS, vol. 4778, Springer, 2007, pp. 168–182. [23] D. Monro, S. Rakshit, D. Zhang, DCT-based iris recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (4) (2007) 586–595. [24] C. Harris, M. Stephans, A combined corner and edge detector, in: ALVEY Vision Conference, 1988, pp. 147–151. [25] K. Mikolajczyk, C. Schmid, Indexing based on scale invariant interest points, in: 8th IEEE International Conference on Computer Vision, vol. 1, 2001, pp. 525–531. [26] C. Valgren, A. Lilienthal, SIFT, SURF and Seasons: long-term outdoor localization using local features, in: 3rd European Conference on Mobile Robotics, 2007. [27] Y. Fang, J. Cheng, K. Wang, H. Lu, Hand gesture recognition using fast multi-scale analysis, in: Proceedings of the Fourth International Conference on Image and Graphics, 2007, pp. 694–698. [28] P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, 2001, pp. 511–518. [29] Casia database. http://www.cbsr.ia.ac.cn/english/Databases.asp. [30] Bath University database. http://www.bath.ac.uk/elec-eng/research/sipg/irisweb. [31] H. Proena, L.A. Alexandre, UBIRIS: a noisy iris image database, in: Proceed. of ICIAP 2005—Intern. Confer. on Image Analysis and Processing, vol. 1, 2005, pp. 970–977.
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Mathematical and Computer Modelling journal homepage: www.elsevier.com/locate/mcm
Fast segmentation and adaptive SURF descriptor for iris recognition Hunny Mehrotra ∗ , Pankaj K. Sa, Banshidhar Majhi Department of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India
article
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Article history: Received 8 March 2011 Received in revised form 1 March 2012 Accepted 30 June 2012 Keywords: Adaptive thresholding Hole filling Adaptive normalization Gamma enhancement SURF Iris biometrics
abstract In this paper a robust segmentation and an adaptive SURF descriptor are proposed for iris recognition. Conventional recognition systems extract global features from the iris. However, global features are subject to change for transformation, occlusion and nonuniform illumination. The proposed iris recognition system handles these issues. The input iris image is used to remove specular highlights using an adaptive threshold. Further, the pupil and iris boundaries are localized using a spectrum image based approach. The annular region between the pupil and iris boundaries is transformed into an adaptive strip. The strip is enhanced using a gamma correction approach. Features are extracted from the adaptive strip using Speeded Up Robust Features (SURF). The results obtained using SURF are compared with the existing SIFT descriptor and the proposed approach performs with improved accuracy and reduced computation cost. © 2012 Elsevier Ltd. All rights reserved.
1. Introduction With the present need of trust for various activities like financial transactions, access to networks and information, there is a strong requirement to develop an authentication system. Iris biometrics plays an important role in providing a promising solution for authentication of an individual using unique texture patterns. The iris is an internal organ whose texture remains stable throughout the life of an individual. The randomness of iris patterns is used to distinguish between individuals. A generic iris biometric system performs localization of the pupil and iris boundaries, generates iris codes and matches the two iris templates to perform authentication. Traditional iris biometric systems assume that the input is an ideal iris image without occlusion or severe transformations. However, the problem is aggravated when noise comes into consideration due to occlusion, non-uniform illumination, change in viewpoint of an individual or head tilt during acquisition. Iris recognition using such noisy images is a challenging problem. There has been considerable research into iris recognition using ideal images. Daugman has proposed an automated iris recognition system in [1]. Existing works on iris recognition mainly focus on analyzing the texture using different approaches. These approaches for recognition can be broadly classified into four categories.
• Texture based method: The texture analysis of local spatial patterns is used to extract features. In [2] a Laplacian of Gaussian filter at multiple scales is used for matching two irises. In [3] the radially smoothed iris image is decomposed in the angular direction using a one dimensional continuous wavelet transform. Iris textures are analyzed in [4] to capture the discriminating frequency information. Specific filters with distinct center frequencies are applied to three different portions to extract the texture features of the iris. Different weights are given to each portion depending on its contribution to recognition. An efficient iris recognition algorithm, obtained through the fusion of the Haar wavelet and the circular Mellin operator has been proposed in [5]. The approach proposed in [6] uses a bank of Gabor filters to capture iris characteristics.
∗
Corresponding author. E-mail address: [email protected] (H. Mehrotra).
0895-7177/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.mcm.2012.06.034
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Fig. 1. Block diagram of proposed iris recognition system.
• Phase based method: In this type of method the phase angles are assigned to the iris pattern by finding the sub-pixel image translation. A Gaussian filter at multiple scales is used to produce a template and computes the normalized correlation as a similarity measure [1]. A phase-based image matching technique for iris recognition using 2D discrete Fourier transforms (DFTs) is given in [7]. • Zero crossing based method: Zero-crossings of the wavelet transform at various resolution levels are calculated over concentric circles on the iris, and the resulting one-dimensional (1D) signals are compared with model features using different dissimilarity functions [8]. • Keypoint descriptors: Local features are based on the appearance of an object at particular interest points and are invariant to image scale and rotation. They are also robust to changes in illumination, noise, and minor changes in viewpoint. This technique of local feature extraction is applied to the iris using a region based SIFT approach [9]. The idea is to develop a keypoint descriptor that is capable of performing well for iris textures. Further, the most significant local extrema of the first two Taylor expansion coefficients as descriptors of the iris texture are proposed in [10]. In [11], a dual stage corner point descriptor is proposed that uses spatial location and entropy information of a window around the corner. In [12] the authors used, another keypoint descriptor, Speeded Up Robust Features (SURF), to perform recognition directly from annular iris images. In [13], the Gabor wavelet is combined with SIFT for feature extraction and the system is tested using frontal and off-angle iris images. The texture based approaches perform well for constrained databases with partial occlusion, uniform illumination and high quality input. However, the texture pattern exhibits significant change in characteristics for variation in scale and illumination. Thus, there is a need to develop a feature extractor that is invariant to various possible transformations and occlusion. Keypoint descriptors are well known approaches for object recognition as proposed by Lowe [14]. To characterize the features in the spatial domain, interest points known as keypoints are detected. A descriptor vector is formed around every detected keypoint to extract features invariant to several transformations. Due to their inherent advantages, local descriptors could find applicability to iris recognition. The proposed iris recognition system provides an improvement over existing approaches at various phases. (i) During preprocessing, a phase adaptive threshold is determined to generate a binary image. The specular highlights lying on the pupil region are detected and removed using a morphological hole filling operation. (ii) During iris normalization, an adaptive strip is generated to overcome sampling artifacts unlike the fixed sized strip as in earlier proposed works. (iii) During enhancement, the adaptive strip is enhanced to reduce the effect of non-uniform illumination. (iv) During feature extraction, SURF features are extracted from an adaptive strip that possesses invariance to various possible transformations and partial occlusion. A block diagram of the proposed approach is shown in Fig. 1. The paper is organized as follows. The preprocessing and enhancement of the iris image are explained in detail in Section 2. From the acquired iris image the pupil and iris boundaries are segmented as given in Section 2.1. The localized image is used to normalize the iris into an adaptive strip to overcome sampling error as given in Section 2.2. Further, the variable sized strip is enhanced to achieve invariance to illumination as given in Section 2.3. The features are extracted from the enhanced adaptive strip using Speeded Up Robust Features (SURF) [15]. The results for the proposed iris recognition system are given in Section 5 and compared with the existing SIFT approach. Finally, the conclusions are given in Section 6. 2. Preprocessing and enhancement In this paper a faster segmentation approach is proposed that performs more accurately in comparison to traditional segmentation approaches. The acquired iris image contains redundant information that should be detected and removed
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Fig. 2. The relationship between τ and η.
to extract the unique features from the iris. During the preprocessing phase, the input iris image is used to find the pupil and iris boundaries. The annular region lying between the two boundaries is normalized into an adaptive sized rectangular block. Finally, the rectangular block is enhanced for feature extraction. A detailed description of the steps involved are given as follows. 2.1. Iris segmentation In this approach iris segmentation is performed using a non-parametric spectrum image approach. The input iris image is binarized using an adaptive threshold. Further, the pupil boundary is localized using a spectrum image approach and the iris circle is delineated using circular summation of intensities. The annular region between the pupil and iris boundaries is transformed into a rectangular block of variable size to overcome aliasing artifacts. This block is adaptive to the size of the iris that varies due to illumination. 2.1.1. Adaptive thresholding The pupil is the darkest region in the eye with almost circular shape. An appropriate threshold helps to determine the region of interest due to the pupil. A static value of the threshold may fail for different images taken under varying illumination conditions [16]. In this paper an effort has been made to adaptively determine the value of the threshold. It has been empirically observed that the highest intensity value contributing to the pupil neither exceeds highτ (0.5 times the highest grayscale value) nor drops beyond lowτ (0.1 times the highest grayscale value). To find the adaptive threshold, binary images are obtained iteratively for a range of thresholds (τ ) between lowτ and highτ with increments of stepτ (0.05 times the highest grayscale value). The parameters are optimized based on a trade off between computational complexity and accuracy. The binary images obtained for varying τ are considered to remove specular highlights (holes). A morphological region filling approach is used to fill holes in the image. To begin the hole filling operation, the binary image (A) is complemented. The convention adopted here is that the boundary pixels are labelled as 1. If non-boundary pixels are labelled as 0 then beginning with a point p inside the boundary a value of 1 is assigned. The following transformation fills the region with ones: Xk = (Xk−1 ⊕ S ) ∩ Ac
(1)
where X0 = p; k = 1, 2, 3, . . .; ⊕ is used for dilation of Xk−1 by S which is defined as Xk−1 ⊕ S = {z |(Sˆ )z ∩ Xk−1 ̸= φ}.
(2)
S is the symmetric structuring element defined as
0 1 0
1 1 1
0 1 . 0
This algorithm terminates at the kth iteration if Xk = Xk−1 . The image generated from the last iteration Xk is combined with A using a bitwise OR that contains the boundary filled image. A graphical representation of the hole filling process is shown in Fig. 3. Each hole filled image is used to find the number of connected components (η). η changes for a change in value of the threshold as shown in Fig. 2. The value of threshold corresponding to minimum non-zero η is chosen as the adaptive
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Algorithm 1 Adaptive_Thresholding Require: I: Intensity Image, S: Structuring element Ensure: B: Binary Image lowτ ⇐ 0.10 highτ ⇐ 0.50 stepτ ⇐ 0.05 [r c ] := size(I) {Compute width and height of image} for τ := lowτ to highτ step stepτ do A := binary(I , τ ) {Image Binarization using τ } C := Ac {Complement of an image} X0 := zeros(r , c ) {Image with all zeros} X0 (p) = 1 {p is a point inside hole} k⇐0 repeat k⇐k+1 Xk ⇐ (Xk−1 ⊕ S ) ∩ C until Xk ̸= Xk−1 Hτ ⇐ Xk ∪ A {Hole filled image} ητ := connComp(Hτ ) {Find no. of connected components} end for pos := min_nonzero(η){Find index of minimum non-zero} B ⇐ Hpos
Fig. 3. A: block of the binary image with holes, Ac : complement of A, X0 : image with the first pixel in the boundary, X1 : image after first iteration, Xk : image after kth iteration, H: hole filled image, B: structuring element.
binarization threshold. However, if the minimum non-zero η occurs for more than one threshold (as shown in Fig. 2), then the maximum threshold amongst them is chosen as the adaptive threshold. The reason behind finding the maximum amongst potential thresholds is that the pupil boundary may contain some intensity values which may not contribute to the connected component of the pupil for lower thresholds. Fig. 4 shows binary images obtained for change in τ . Algorithm 1 describes the steps involved. 2.1.2. Pupil detection The traditional segmentation approach [17] finds the pupil and iris circles using the circular Hough transform [18]. The major drawback of the Hough transform is that it requires a range of radius as input from the user and performs localization in R3 parameter space (number of parameters required to describe the shape of a circle) which implies high time complexity of the transform. In this paper a faster approach is used to find the pupil circle without any pre-estimation of the radius range as input. The proposed approach is non-parametric in the sense that it does not require any prior approximation of the circle parameters. The binary image is re-complemented to form the spectrum image [19]. The distance of every pixel in the binary image is obtained with nearest non-zero pixel. By computing the distance between non-zero pixels, the spectrum showing the largest filled circle can be formed within the set of foreground pixels. Since the pupil is the largest filled circle in the image the overall intensity of this spectrum peaks in the center. The spectrum image is shown in Fig. 5(a). Thus, the position
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(a) τ = 0.10; η: 0.
(b) τ = 0.15; η: 0.
(f) τ = 0.35; η: 23.
(c) τ = 0.20; η: 1.
(g) τ = 0.40; η: 18.
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(d) τ = 0.25; η: 1.
(h) τ = 0.45; η: 23.
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(e) τ = 0.30; η: 32.
(i) τ = 0.50; η: 30.
Fig. 4. Binary images obtained for change in threshold (τ ) and number of connected components (η).
a
b
c
Fig. 5. Pupil detection. (a) Spectrum image. (b) Edge detected image with pupil center. (c) Pupil localized image.
of the maximum value in the spectrum image is the pupil center. To compute the pupil radius, an edge map of the hole filled binary image is obtained as shown in Fig. 5(b). In the edge map, the distance from the detected pupil center to the nearest non-zero pixel is the pupil radius. The pupil detected image is shown in Fig. 5(c). The algorithm for detecting the pupil center and radius is given as Algorithm 2.
Algorithm 2 Pupil_Detection Require: B: Binary Image Ensure: xc : xcenter of pupil, yc : ycenter of pupil, rp : Radius of pupil C ⇐ Bc {Complement the binary image} [x y] := find(C == 1) {Find location of ones in an image} l := length (x) {To find the no. of elements in an array} for i := 1 to r do for j := 1 to c do for k := 1 to l do Dk ⇐ (xk − i)2 + (yk − j)2 end for Si,j := min(D) {Minimum value of D} end for end for [xc yc ] ⇐ max(S ) E := edge(C ) {Edge detection} j ⇐ yc {Estimation of pupil radius} rp ⇐ 0 while Exc ,j ̸= 1 do rp ⇐ rp + 1 j⇐j+1 end while
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Fig. 6. Iris detection. (a) Contrast enhanced image. (b) Concentric circles of different radii. (c) Iris localized image.
2.1.3. Iris detection For iris detection the input iris image is blurred to remove external noise. But too much blurring may make it difficult to detect the outer iris boundary, separating the eyeball and sclera. Thus, a special smoothing filter such as a median filter is used on the original intensity image. This type of filtering eliminates sparse noise while preserving image boundaries [20]. After filtering, the contrast of the image is enhanced to have sharp variation at the image boundaries using histogram equalization as shown in Fig. 6(a). This contrast enhanced image is used for finding the outer iris boundary by drawing concentric circles (Fig. 6(b) shows an example) of different radii from the pupil center and the intensities lying over the perimeter of the circle are summed up. Among the candidate iris circles, the circle having maximum change in intensity with respect to the previously drawn circle is the iris outer boundary as shown in Fig. 6(c). The algorithm for detection of the iris radius is given as Algorithm 3. Algorithm 3 Iris_Detect Require: I: Input image, rp : radius of pupil, xc : xcenter of pupil, yc : ycenter of pupil Ensure: ri: radius of iris F ⇐ medianFilt(I){Median Filtering on input image} H ⇐ Histeq(F) {Histogram equalization} [r c ] ⇐ size(I) {Finding image dimensions} {Finding the intensity over circumference} for ri = rp × 1.5 to 2r do sumri ⇐ 0 for θ = 0 to 360 do x = xc + ri × cos(θ ) y = yc + ri × sin(θ ) sumri = sumri + Hx,y end for ri = ri + 2 end for {Change in intensity over circumference} for i = 1 to ri do Di = |sumi − sumi+1 | end for [d ri] = max(D) {Maximum change in intensity}
2.2. Adaptive normalization In texture recognition approaches the extracted features should be invariant to change in scale, position and orientation. Thus the annular region between the pupil and iris boundaries is normalized to achieve the aforementioned invariance. There exists a traditional approach to iris normalization [1] that transforms the Cartesian coordinates to a doubly dimensionless polar coordinate system. The main objective is to maintain reference to the same region of iris texture irrespective of camera to eye distance and expansion/dilation of the pupil. The rubber sheet model as proposed by Daugman [1] assigns to each point in the iris regardless of size and pupillary dilation a pair of dimensionless real coordinates. However, the size of the pupil is controlled by muscles in the iris which control the amount of light entering the eye. Thus the pupil shrinks in bright light and expands in low light. The variable size of the pupil causes linear deformation of the texture information and creates an aliasing effect. Further, the change in distance between the camera lens and the user’s eye scales the features. In addition to this, during acquisition there may be a slight tilt in the user’s head. This rotates features in a
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Fig. 7. The effect of aliasing on iris normalization using fixed and adaptive strips.
circular direction. Normalizing the distance between pupil and iris boundary creates an aliasing effect in the iris texture [21]. The conversion into dimensionless coordinates introduces linear deformation of the texture. Thus, transformation scales the features and deteriorates the performance of the system. Fig. 7 shows two instances of the same eye taken from the CASIAV3 database under varying illumination conditions. The strips generated using traditional approaches are of fixed size (100×360 pixels) as given in Fig. 7(a.1) and (b.1) respectively. From the images it is evident that although the region lying between the pupil and iris boundaries is not uniform in both the images, the texture features are scaled to some constant size to render a scale invariant image. This can be regarded as sampling the data with the possibility of aliasing [21]. To overcome aliasing artifacts, the proposed scale based approach normalizes the iris image by converting it from Cartesian space to non-uniform (dimension dependent) polar space. The points lying on the perimeter of the iris and pupil circle are obtained using xp (θ ) = xc + rp × cos(θ ) yp (θ ) = yc + rp × sin(θ )
(3)
xi (θ ) = xc + ri × cos(θ ) yi (θ ) = yc + ri × sin(θ ) where xp (θ ) and yp (θ ) are the points lying on the pupil circle, xi (θ ) and yi (θ ) are the points lying on the iris circle, while rp and ri are the pupil and iris radius respectively. The variant to normalization is achieved by taking the range of radius between the pupil and iris boundaries and mapping the points to a rectangle using the actual distance (ri − rp ). The mapping equations are given as follows: x(ρ, θ ) = (1 − ρ)xp (θ ) + ρ × xi (θ ) y(ρ, θ ) = (1 − ρ)yp (θ ) + ρ × yi (θ )
(4)
where ρ is given in the interval of [0 : ρinc : 1] and ρinc is defined by
ρinc =
1 ri − rp
.
(5)
The images given in Fig. 7(a.2) and (b.2) are generated using the adaptive normalization approach. Here the strip is formed depending upon the actual distance between the inner and outer boundaries. Thus, the features do not undergo any deformation due to scaling. The algorithm for iris normalization using the proposed approach is given as Algorithm 4. 2.3. Strip enhancement Enhancement is carried out to overcome the effect of non-uniform illumination, shadowing and highlights in the strip. The objective is to remove noise while still preserving essential texture information. The process starts with gamma correction that is defined by
IE =
Iγ log(I )
if γ ≥ 0 if γ = 0
(6)
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Algorithm 4 Adaptive_Normalization Require: I: Input image, rp : radius of pupil, ri : radius of iris, xc : xcenter of pupil, yc : ycenter of pupil Ensure: N: Normalized Image for θ = 0 to 360 do xp (θ ) = xc + rp × cos(θ ) yp (θ ) = yc + rp × sin(θ ) xi (θ ) = xc + ri × cos(θ ) yi (θ ) = yc + ri × sin(θ ) end for
ρinc = ri −1rp for ρ = 0 : ρinc : 1 do for θ = 0 to 360 do x = (1 − ρ) × xp (θ ) + ρ × xi (θ ) y = (1 − ρ) × yp (θ ) + ρ × yi (θ ) N (ρ, θ ) = I (x, y) end for end for
where γ ∈ [0 : 1], I is the normalized iris image and IE is the image after gamma correction as shown in Fig. 8(b). Gamma correction has a characteristic of enhancing local dark regions while compressing bright regions and highlights [22]. However, Gamma correction does not remove the shading effects caused by eyelashes and eyelids. High pass filtering removes useful as well as incidental information. Similarly, suppressing the highest spatial frequencies reduces aliasing. Difference of Gaussian (DOG) filtering is used to achieve these effects. Given an enhanced input image IE , the Gaussian blurring is obtained by L(x, y, σ ) = IE (x, y) ∗ G(x, y, σ )
(7)
where G(x, y, σ ) =
1 2π σ
2
exp
−(x2 + y2 ) . σ2
(8)
The result of convolving an image with a difference of Gaussian filter is given by D(x, y) = L(x, y, σ1 ) − L(x, y, σ2 )
(9)
where σ1 is the outer Gaussian and contains more pixels (≈2–4 pixels) and σ2 is the inner Gaussian and is quite narrow (one pixel). The filtered image is shown in Fig. 8(c). Further, after DOG filtering the intensity values are rescaled to standardize the intensity variation and discard some extreme values as given in [22]. A simple approximation using a two stage process is given by I ( x, y ) = I ( x, y ) =
D(x, y)
(10)
1
(mean(|I (x, y)|a )) a I (x, y)
1
(mean(min(τ , |I (x, y)|)a )) a
(11)
where a is an exponent that reduces large values and τ is the threshold which is used to truncate large values after transformation as given in Eq. (10). The resultant image is well scaled but may still contain some extreme values, thus a hyperbolic tangent is used to discard these values as given by I (x, y) = τ tanh
I ( x, y )
τ
.
(12)
The contrast adjusted image obtained from Eq. (12) is in the range [−τ : τ ]. This image is further rescaled to be in the range [0 : 1] using min–max normalization. The contrast adjusted image is shown in Fig. 8(d). Further histogram equalization is applied on the resultant image to get the enhanced image as shown in Fig. 8(e). 3. Speeded up robust features for the adaptive strip The iris strip is categorized as repeated occurrence of texture elements known as texels. Texels in an image are statistically similar but not identical. As proposed in Section 2.2, the normalized iris image is of variable size hence it is not well suited for matching using global transforms like DCT [23], Haar [8], gabor wavelets [1], etc. Further, the relative positions of texels change and cannot be mapped to the same locations in polar coordinates without some transformation [9]. Thus,
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(a) Input image.
(b) Gamma corrected.
(c) DOG filter.
(d) Contrast equalisation.
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(e) Histogram equalisation. Fig. 8. Intermediate images obtained during enhancement of the iris strip.
some local feature extraction technique is required that is invariant to change in scale, rotation, occlusion and viewpoint of two iris images. The local features around special points known as keypoints/keytexels are obtained and compared to find the similarity between the images. The most valuable property of a keypoint detector is its repeatability, i.e. whether it reliably finds the same interest points under different viewing conditions [15]. To extract features around keytexels the neighborhood of every detected point is represented by a feature vector (descriptor). This descriptor has to be distinctive and at the same time robust to transformations, illumination and partial occlusions. A considerable amount of work has been carried out for local feature detection. The most widely used interest point detector is the Harris corner detector, based on eigenvalues of the second moment matrix [24]. The Harris corner detector is applied to the iris to extract corner points and the entropy information of a window around the corner is taken as the descriptor [11]. However, Harris corners are not scale invariant and fail to achieve the property of repeatability when the iris strip scales due to illumination or change in the camera to eye distance. Thus, the technique is found to be unsuitable for scale based normalization. Further, an adaptive scale based Harris measure has been proposed in [25] where the determinant of a Hessian matrix is used to select the location, and a Laplacian to select the scale. A detailed study of detectors has been made in [15] and it has been concluded that Hessian-based detectors are more stable and repeatable than Harris-based detectors. However, the location of interest points as features does not yield satisfactory results because it has been inferred that the locations may undergo change due to transformations. Thus, a feature descriptor termed the Scale Invariant Feature Transform (SIFT) has been proposed that stores a substantial amount of information around the interest points [14] using an orientation histogram. SIFT features have proved to perform with high accuracy along with low computation time. Region based SIFT has been proposed for annular iris images [9]. The system performs well for non-cooperative iris databases as well. In this paper, the most promising approach, Speeded Up Robust Features (SURF) [15], is used which performs better compared to SIFT with low computational cost. SURF uses the same matching approach as SIFT but with a few variations. Firstly, SURF uses the sign of the Laplacian to have a sharp distinction between background and foreground features. Secondly, SURF uses only 64 dimensions compared to SIFT using a 128 dimensional vector. This reduces feature computation time and allows quick matching with increased robustness simultaneously [26]. The SURF operator extracts keytexels using a Hessian matrix and describes a distribution of Haar wavelet responses from a window around the interest point as descriptors. These features are found to be very distinctive and stable. SURF is found to be far more stable and unique compared to state of the art approaches. SURF when applied to an adaptive strip is termed an adaptive SURF descriptor. A detailed description of iris feature extraction using SURF is given as follows. 3.1. Keytexel detector For detection of keytexels the determinant of a Hessian matrix is used for selecting location and scale. Given a point P = (x, y) in an image I, the Hessian matrix H (P , σ ) in P at scale σ is defined as follows: L (P , σ ) H (P , σ ) = xx Lxy (P , σ )
Lxy (P , σ ) Lyy (P , σ )
where Lxx (P , σ ) is the convolution of the Gaussian second order derivative
(13)
σ2 g (σ ) σ x2
with the image I at the point P and
Lxy (P , σ ) and Lyy (P , σ ) are obtained similarly. The discrete Gaussian derivatives and their approximations for a 9 × 9 box
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Fig. 9. Detected texel points on an adaptive strip for two instances of the same individual taken from the CASIAV3 database.
filter at σ = 1.2 are denoted by Dxx , Dxy and Dyy [27]. Image convolutions with the box filters are computed rapidly using integral images [28]. The integral image at location (x, y) is defined as sum of all pixels above and left of (x, y) Is (x, y) =
y x
I (i, j)
(14)
i =0 j =0
where Is is the integral image for the input image I. By choosing the weights for the box filters adequately, the approximations for the Hessian’s determinant are computed using Det(Happrox ) = Dxx Dyy − (0.9Dxy )2 .
(15)
Keytexels are localized in scale and image space by applying a non maximum suppression in a 3 × 3 × 3 neighborhood. The local maxima found on the determinant of the Hessian matrix are interpolated to image space. 3.2. Keytexel descriptor A circular window is constructed around every detected texel point and orientation is estimated using Haar wavelet responses in both horizontal and vertical directions. The orientation helps to have invariance to rotation. Further, SURF descriptors are obtained by taking a rectangular window around every detected keytexel in the direction of orientation obtained earlier. The windows are split into 4 × 4 sub-regions to take into consideration the spatial information. In each subregion Haar wavelet responses extracted in the horizontal and vertical directions (dx and dy ) are summed up. The wavelet responses are summed up along with the absolute values to find the polarity of image intensity changes. The feature vector of each sub-image is given by V =
dx ,
dy ,
|dx |,
|dy | .
(16)
Thus, by summing up the descriptor vectors from all 4 × 4 sub-regions, a feature descriptor of length 64 is obtained. The descriptor vector of length 64 for each keytexel is known as the keytexel descriptor. Fig. 9 shows the number of keytexels detected using two different instances of the same individual from the CASIAV3 database [29]. The number of detected texel points varies with change in the size of the strip. This highlights the effect of illumination on the feature extractor. The features may undergo transformation due to rotation, scaling and occlusion due to eyelids. Thus, there is a need to use a feature descriptor that posses invariance to the aforementioned issues. SURF is one such very reliable keypoint descriptor that has been applied to iris recognition for better accuracy. 4. Keypoint pairing After detection of keytexels in database image (A) and query image (B), matching is carried out using a point pairing approach. The best candidate match for each texel point in A is found by identifying the closest pair from the set of texel points in B. The nearest neighbor is defined as the texel point with minimum Euclidean distance for the invariant descriptor vector. Let L = {l1 , l2 , l3 , . . . , lm } and E = {e1 , e2 , e3 , . . . , en } be vector arrays of keytexels of A and B respectively obtained through SURF. The descriptor array li of texel point i in L and descriptor array ej of texel point j in E are paired if the Euclidean distance ∥li − ej ∥ between them is less than a specified threshold ψ . Threshold based pairing results in several matching points. To avoid multiple matches, the keytexels with minimum descriptor distance and less than the threshold are paired. This results in a single matching pair, and is called the nearest neighborhood matching method. In SURF, the matching method applied
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Table 1 Mis-localization percentages of Masek’s approach and the proposed approach. Database
Masek
Proposed
BATH CASIAV3 UBIRIS
37.62 05.23 10.53
0.98 0.45 3.41
Table 2 Average localization time taken (in seconds) for the proposed approach and Masek’s approach. Approach
Masek [17] Proposed
Databases BATH
CASIAV3
UBIRIS
02.2820 00.3383
13.0676 00.3960
01.7656 00.2820
is similar to nearest neighbor matching, except that the thresholding is applied to the descriptor distance ratio between keytexels. The method used in SURF is called the nearest neighbor ratio method. Thus, the texel points are matched if
∥li − ej ∥/∥li − ek ∥ < ψ
(17)
where ej is the first nearest neighbor and ek is the second nearest neighbor of li . The paired points (li , ej ) are removed from L and E respectively. The matching process is continued until there are no more key texels. Based on the number of pairs between the live image B and enrolled image A, a decision about the candidate’s identity is taken. 5. Experimental results To measure the performance of the iris recognition system experimental results are obtained on various available datasets such as BATH [30], CASIA-IrisV3 (CASIAV3) [29] and UBIRIS [31]. The database available from Bath University comprises images from 50 subjects. For each subject both left and right iris images are obtained, each containing 20 images of the respective eyes. CASIAV3 is acquired in an indoor environment. Most of the images are captured in two sessions, with at least one month interval. The database contains 249 subjects with a total of 2655 images from left and right eyes. The UBIRIS.v1 database is composed of 1877 images collected from 241 persons in two distinct sessions [31]. The main characteristics of the database are that it incorporates several noise factors to measure the performance of iris recognition systems. The results are obtained for the proposed segmentation and novel feature descriptor in two different stages. 5.1. Segmentation performance At the first level, the performance of the proposed localization approach is compared with Masek’s benchmark approach [17]. Masek’s approach uses a circular Hough transform for detection of the pupil and iris boundaries. Table 1 gives the mis-localization percentage by the proposed approach and Masek’s approach. The proposed approach gives a mis-localization of 3.41% on the UBIRIS noisy iris database with significantly low mis-localizations of 0.98% and 0.45% on the BATH and CASIAV3 databases. Masek’s approach performs with considerably high mis-localization percentage for the BATH database and noisy UBIRIS database. Fig. 10 shows localized iris images using Masek’s approach and the proposed approach. Masek’s approach localizes the inner pupil boundary accurately for most samples from the CASIAV3 database but fails to localize the outer iris boundary. However, errors due to localization increase for the noisy and low resolution BATH and UBIRIS databases. Thus, Masek’s approach fails for low resolution images whereas the proposed approach possesses invariance to rotation, viewpoint and quality of the input image. The average times taken in seconds/image using the proposed approach and Masek’s approach are given in Table 2. The results are generated using an AMD Athlon dual core processor with 2.81 GHz processor speed and 2.00 GB of RAM. The experimental results show that the proposed approach performs considerably faster compared to Masek’s approach. The Hough transform performs circle fitting for radius range, which is quite expensive for the high resolution images from the CASIAV3 database. The proposed approach finds the circle parameters in considerably less time with better accuracy. The segmentation approach performs localization in 00.3960 s/image in comparison to Masek’s approach which performs localization in 13.0676 s/image. 5.2. Recognition performance At the second level of the experiment the accuracy results are obtained using the proposed segmentation approach. The adaptive strip is enhanced and features are extracted using SURF and SIFT. Table 3 shows the accuracy values obtained on the adaptive strip after enhancement. SIFT performs poorly with an accuracy of 77% on the noisy UBIRIS database whereas the accuracy improves significantly to 96.91% using the adaptive strip. The accuracy improves from 85.69% for a fixed strip
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Fig. 10. Localization results using Masek’s approach and the proposed approach.
Table 3 Results generated using SIFT and SURF on fixed and adaptive strips. Approach
Database UBIRIS
SIFT SURF
Fixed strip Adaptive strip Fixed strip Adaptive strip
BATH
CASIAV3
FAR
FRR
Accuracy
FAR
FRR
Accuracy
FAR
FRR
Accuracy
13.08 6.16 7.34 5.02
32.91 0.00 4.11 1.80
77.00 96.91 94.27 96.58
8.01 4.56 7.98 1.44
10.37 5.54 3.46 2.06
90.80 94.94 94.27 98.24
7.16 3.74 3.39 1.55
19.64 4.34 4.78 3.80
86.59 95.95 95.91 97.32
Table 4 Average recognition time taken (in seconds) for fixed and adaptive strips. Test cases
Databases BATH
SIFT SURF
CASIAV3
UBIRIS
Fixed
Adaptive
Fixed
Adaptive
Fixed
Adaptive
0.977 0.156
0.543 0.053
0.911 0.267
0.899 0.154
0.563 0.282
0.368 0.043
to 95.95% for an adaptive strip using SIFT on the CASIAV3 database. The system is marginally acceptable for the constrained images in the BATH database. Further, SURF is applied on fixed and adaptive strips of irises and results are obtained. The system performs with an accuracy of 94.27% using a fixed strip on the UBIRIS database. The accuracy increases to 96.58% after taking an adaptive size of strip. Similar observations are made for the CASIAV3 database. The gain in accuracy is significant for the constrained BATH iris database. The average times taken in seconds using SIFT and SURF are given in Table 4. The SIFT feature descriptor takes 0.977 s/image for the BATH database using a fixed strip. The time reduces to 0.543 s/image after using an adaptive strip. Similar results are computed for the CASIAV3 and UBIRIS databases. However, the time required to perform recognition using SURF is extremely low for an adaptive strip on the various available databases. The receiver operating characteristic (ROC) is the plot of false acceptance rate (FAR) versus genuine acceptance rate (GAR). A plot of ROC is obtained for SIFT on the various available databases as shown in the left column of Fig. 11. Similarly, an ROC curve is obtained for SURF as shown in the right column of Fig. 11. From the results it is observed that the adaptive SURF descriptor performs with higher accuracy in less time. The distributions of genuine and imposter scores for adaptive SIFT and adaptive SURF are shown in Fig. 12. 6. Conclusion In this paper an effort has been made to develop a robust iris recognition system that possesses invariance to various possible transformations, occlusion and illumination. The proposed segmentation approach is compared to the benchmark
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Fig. 11. ROC curve for SIFT and SURF using fixed and adaptive strips.
localization approach used by Masek [17]. The proposed approach performs faster with better accuracy. Further, during normalization the iris is normalized to generate an adaptive strip for feature extraction. This adaptive strip is enhanced to overcome the effect of non-uniform illumination. For feature extraction, a well known keypoint descriptor named SURF is used. The adaptive iris recognition system performs with an accuracy of 96.58% on the noisy UBIRIS database and with
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Fig. 12. Distribution of genuine and imposter scores for the adaptive strip.
accuracies of 98.24% and 97.32% on the BATH and CASIAV3 databases. The time required to claim identification using SURF is comparatively low compared to SIFT. From the results it is found that the proposed system performs faster with improved accuracy. Thus, the system can be used for highly trust based applications such as financial transactions and security.
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