An approach for iris contact lens detection and classification using ensemble of customized DenseNet and SVM

Future Generation Computer Systems 101 (2019) 1259–1270

Contents lists available at ScienceDirect

Future Generation Computer Systems journal homepage: www.elsevier.com/locate/fgcs

An approach for iris contact lens detection and classification using ensemble of customized DenseNet and SVM ∗

Meenakshi Choudhary, Vivek Tiwari , Venkanna U. DSPM International Institute of Information Technology (IIIT), Naya Raipur (C.G.), India

highlights • • • • •

An Iris Contact Lens Detection and Classification problem has been considered. The ensemble of Customized DenseNet and SVM approach has been proposed. Softmax classifier has been replaced with SVM, which given the better performance. IIIT-Delhi Contact Lens and Notre Dame Contact Lens 2013 Database has been used. Proposed model improves Correct Classification Rate (CCR) up to 4%.

article

info

Article history: Received 24 December 2018 Received in revised form 5 April 2019 Accepted 4 July 2019 Available online 1 August 2019 Keywords: Contact lens detection DenseNet Support Vector Machine (SVM) Iris normalization Convolutional Neural Network (CNN)

a b s t r a c t In spite of the prominent advancements in iris recognition, it can significantly be deceived by contact lenses. As the contact lens wraps the iris region and obstructs sensors from capturing the actual iris. Moreover, cosmetic lenses are prone to forge the iris recognition system by registering an individual with fake iris signatures. Therefore, it is foremost to perceive the existence of the contact lens in human eyes prior to access an iris recognition system. This paper introduces a novel Densely Connected Contact Lens Detection Network (DCLNet) has been proposed, which is a deep convolutional network with dense connections among layers. DCLNet has been designed through a series of customizations over Densenet121 with the addition of Support Vector Machine (SVM) classifier on top. It accepts raw iris images without segmentation and normalization, nevertheless the impact of iris normalization on the proposed model’s performance is separately analyzed. Further, in order to assess the proposed model, extensive experiments are simulated on two widely eminent databases (Notre Dame (ND) Contact Lens 2013 Database and IIIT-Delhi (IIITD) Contact Lens Database). Experimental results reaffirm that the proposed model improves the Correct Classification Rate (CCR) up to 4% as compared to the state of the arts. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Human iris is a prevalent biometric trait due to its complex texture with discriminative features [1,2]. It is widely used for person recognition and authentication in several large scale control applications, such as India’s Aadhaar program [3], Amsterdam’s airport [4] and US/Canadian borders [5], etc. Although, the Iris Recognition (IR) is a longstanding field, yet the real-time automated IR system was discovered by Daugman in 1993 [6]. Iris recognition is robust and driving, however, it significantly gets influenced by surrounding factors, such as pupil dilation [7] ∗ Corresponding author. E-mail address: [email protected] (Vivek T.). https://doi.org/10.1016/j.future.2019.07.003 0167-739X/© 2019 Elsevier B.V. All rights reserved.

and sensor interoperability [8,9]. Consequently, various improvements [10–14] were carried out to address these issues. Moreover, the iris recognition system can be forged through the use of contact lenses that are available in two diverse varieties, i.e. Transparent (Soft) and Textured (Cosmetic or Colored). As Soft lenses are transparent, they do not modify the iris texture, yet may ominously change the reflection property of the iris region [15]. Moreover, the contours of soft lenses lying between sclera and boundary of iris may cause recognition failure. On the other hand, the cosmetic lens superimposes high-intensity color on iris and hides the actual texture with external texture printed on it. Due to the aforementioned characteristics of both contact lenses, they can be exploited to spoof the iris recognition system [16]. Exploiting such characteristics, an individual may register himself with the fake iris signature. In this view, contact

1260

Meenakshi C., Vivek T. and Venkanna U. / Future Generation Computer Systems 101 (2019) 1259–1270

representation. Yet, conveyed diminished performance because of the shallow architecture. To counter this, Raghavendra et al. [30] proposed a CNN model with 15 layers namely ‘ContlensNet’ that is trained using multiple patches of normalized iris images. However, it requires pre-processing of iris images before feeding to the model. On the other hand, the GHCLNet [31] produces better results without normalization and pre-processing. Nevertheless, it uses a hierarchy of two identical ResNet50 [35] models each with 170 layers, therefore this model is computationally expensive. 1.1. Problem formulation Fig. 1. Depiction of iris images with cosmetic, Normal and soft lenses.

lens detection is considered as a challenge for the research community and became a performance influencing factor for an IR system. Fig. 1 shows iris images with Cosmetic-lens, No-lens and Soft-lens respectively. A broad collection of techniques have been introduced in literature to accomplish contact lens detection. Yadav et al. [16] attempts to analyze the influence of contact lens on iris recognition and proposed a region based Local Binary Pattern (LBP) method to reduce it. Daugman [17] suggested the Fast Fourier Transform (FFT) for identifying printed iris patterns. This method explores the frequency domain for high-frequency spectral magnitude, which can be represented for printed patterns due to periodic dot printing. However, this method fails for defocused or blurred iris image, and incapable to cope with novel contact lenses with multiple layers of printing. To overcome this, Lee et al. [18] attempts to create a Purkinje image, focused enough for recognition using collimated Infra-Red Light Emitting Diode (IR-LED). In addition, the authors proposed an iris recognition camera with wavelength 760 nm and 880 nm and the reported False Recognition Rate (FRR) was 0.33%. However, this method demands additional hardware. Besides, He et al. [19] employed statistical textural analysis to extract four distinctive features i.e. homogeneity, correlation, contrast, and angular second moment (energy) using Gray Level Co-occurrence Metrics (GLCM), whereas, the Support Vector Machine (SVM) is used for classification among fake and genuine iris. They achieved 100% Correct Classification Rate (CCR) on Shanghai Jiao Tong University (SJTU) iris dataset v3.0. However, this method considers only the lower half portion of eye images for feature extraction. Further, by employing a similar method, Wei et al. [20] used Iris-Textons, Iris edge sharpness, and Co-occurrence matrix to identify forged iris. The reported CCR was between 76.8% and 100% on CASIA and BATH databases. He et al. [21] attempts to identify LBP for several iris sub-region, then train a model using Adaboost learning to recognize the peak discriminative LBP feature. The authors achieved 2.64% FRR on CASIA Iris v3 and ICE v1.0 databases. Zhang et al. [22] employed Scale-Invariant Feature Transform (SIFT) descriptor to explore statistical features and the SVM classifier is used to differentiate between forged and genuine iris. They reported a CCR of 99% and 88% for combined and cross camera validation respectively. Nevertheless, all of the above-mentioned techniques along with [23–28] employs handcrafted feature extraction from iris images, along with hand-coding methods to generate iris templates from these features. In other words, they do not support self/automatic feature learning (see Table 1). In the most recent advancements in the discipline, researchers [32,33] have begun using Convolutional Neural Networks (CNN) to support automatic feature extraction from iris images and classify them to the correct lens category using some classifiers as Softmax, SVM [34], etc. Especially, Silva et al. [29] constructed a CNN model with an added Softmax classifier for deep image

To the best of our knowledge, ContlensNet [30] and GHCLNet [31] are the state-of-the-art architectures based on deep learning, exhibiting best performances in Contact Lens Detection. The ContlensNet is computationally less expensive, yet requires iris normalization and pre-processing. Conversely, the GHCLNet is free from the iris normalization, but possess complex architecture with huge trainable parameters due to the hierarchy of two ResNets. Consequently, there is an urge for a framework which compensates between the duos, i.e. the model should contain simple and feasible architecture, less computationally expensive, with fewer layers and parameters, avoid pre-processing and normalization, and achieves comparable performance. 1.2. Research contribution In this paper, a Densely Connected Contact Lens Detection Network, namely ‘DCLNet’ has been introduced for detecting contact lenses in iris images captured from heterogeneous sensors. The underlying research work is three-fold: a. The proposed work customizes DenseNet121, a preeminent CNN model pre-trained on ImageNet (animal dataset). As iris features substantially differ from animals, this model is fine-tuned to learn complex patterns in Near Infra-Red (NIR) iris images. b. In order to analyze the impact of iris normalization on the proposed model’s performance, Daugman’s Rubber Sheet Model [1] is employed to generate normalized iris images as training and probe set. Further, the model is tested in two phases; with raw iris images and with normalized iris images. A comparative study is performed to analyze the respective performances. Further, for model assessment, two measures are taken as performance indicators, i.e. Correct Classification Rate (CCR) and Receiver Operating Characteristics (ROC) curves. c. In order to ascertain the aid of specific layers of the proposed framework, the layer-specific feature analysis is carried out through the visualization of the iris features learned by arbitrary layers. It helps in selecting the layers of a pre-trained model to fine-tune for iris contact lens detection. The subsequent part of the paper is structured as; Section 2 delineates the proposed model’s background, architecture and working. Section 3 delineates the experimental results, performance comparison with existing models and layer-specific analysis. Finally, Section 4 concludes the work. 2. Proposed model: Densely Connected Contact Lens Detection Network (DCLNet) The proposed model incorporates DenseNet121 [36] as a basic building block with major customization. DenseNet is best suitable for aforesaid problem due to some important observations as it facilitates feature reuse at each subsequent layer inside a dense block and constitutes more feature maps. It also resolves the vanishing gradient problem using a direct path to all preceding layers in order to route residuals during backpropagation. Furthermore,

Meenakshi C., Vivek T. and Venkanna U. / Future Generation Computer Systems 101 (2019) 1259–1270

1261

Table 1 Summarized literature survey. Authors

Databases

Methods used

Results

Remarks

Yadav et al. [16] Daugman [17]

IIITD + ND Live iris images

Region based LBP FFT

CCR ≈ 60%–100% NA

Lee et al. [18] He et al. [19] Wei et al. [20] He et al. [21] Zhang et al. [22] Silva et al. [29] Raghvendra et al. [30] Singh et al. [31]

Live iris images SJTU dataset v3.0 CASIA + BATH CASIA v3 + ICE v1.0 Self-collected databases IIITD contact dataset ND + IIITD ND + IIITD

Dual IR-LED, Collimated IR-LEDs GLCM Iris Texton Co-occurrence Matrix LBP SIFT descriptors+ LBP + SVM CNN + Softmax 15 layer CNN + Softmax ResNet50 + Softmax

FRR CCR CCR FRR CCR CCR CCR CCR

Handcrafted features Not suitable for contact lenses with multiple layer printing Additional hardware requirement Considers only lower half region Lower half region is considered Upper and lower half region is examined Handcrafted features Shallow architecture Needs normalization and preprocessing Computationally expensive

≈ ≈ ≈ ≈ ≈ ≈ ≈ ≈

0.33% 100% 76.8%–100% 2.64% 99% 80%–86% 68%–100% 65%–100%

Abbreviation- CCR: Correct Classification Rate, FRR: False Recognition Rate.

Fig. 2. (a) Residual connection, and (b) Dense block.

it outperformed another state of the art CNN architectures in ImageNet challenge [37]. DenseNet was induced from ResNet [35], where a layer receives outputs from the previous second or third layer through residual connections and the outputs are added on the same depth as shown in Fig. 2(a). During back-propagation, these residual connections route residuals (errors) directly to the previous layers to let network learn faster. DenseNet uses a series of dense blocks with transition layers between them. Fig. 2(b) shows a dense block where a separate connection exists between each layer to all subsequent layers inside a block. Hence, a dense block with L layers has a total L(L + 1)/2 connections among them [36]. In ResNet, the output of layer-l is x[l] = f (w ∗ x[l − 1] + x[l − 2])

(1)

whereas, in DenseNet x[l] = f (w ∗ h(x[l − 1], x[l − 2], . . . x[1]))

(2)

Here, h denotes stacking of layers. DenseNet is considered better then ResNet in the manner that; it concatenates features as opposed to ResNet which adds them up. Moreover, DenseNet constitutes immense features and has fewer learning-parameter, in fact less than half as compared to ResNet. These observations raise motivation to apply DenseNet for contact lens detection. In dense block, the nth layer gets feature-maps of all previous layers, X0 , X 1 , . . . X n−1 as input. Therefore, the feature map of the nth layer represented by: Xn = Fn ([X0 , X1 , . . ., Xn−1 ])

(3)

Here [X0 ,X 1 , . . . , X n−1 ] constitutes the concatenation of all previous feature-maps output in layer 0, . . ., n − 1. Fn is a function performing three subsequent operations, i.e. BN →ReLU →Conv(3×3) . These represent Batch normalization (BN), Rectified Linear Unit function (ReLU) and Convolution respectively [36]. 2.1. Model designing and fine-tuning The Schematic design of the proposed model is illustrated in Fig. 3 where DenseNet acts as a feature extractor and SVM is a classifier. DenseNet121 is a deep CNN model pre-trained on ImageNet dataset, which contains 4 dense blocks containing 6, 12, 24 and 16 dense layers respectively. Each dense layer constitutes k feature maps as output yet receives comparatively more inputs. Besides, a bottleneck layer is introduced between each dense layer which performs 1 ∗ 1 convolution. Therefore, a dense block has the following arrangement of layers, BN →ReLU →Conv(1×1) →BN →ReLU →Conv(3×3). At the end of the second dense block, a transition layer is used that performs convolution with the kernel size of 1 ∗ 1 and average pooling [36]. Therefore, this transition layer is defined by BN →Conv(1×1) →AvgPooling(2×2) . Collectively, these constitute over 400 layers (including BN, ReLU, dropouts, etc.). Since this model is huge and could not be trained with available iris images in the dataset, DCLNet selects only up to first two dense blocks (with approx. 50 layers) for feature extraction. The output of the pooling layer (pool_2) after the second dense block represents the iris features learned by DCLNet. Further, a flatten layer is added to form a 401 408-dimensional feature vector. For further down-sampling, two fully connected (fc) layers are added with 512 and 128 neurons respectively. In addition, two dropout

1262

Meenakshi C., Vivek T. and Venkanna U. / Future Generation Computer Systems 101 (2019) 1259–1270

Fig. 3. Schematic view of proposed DCLNet.

layers (with probability 0.4) are also used to remove overfitting in fc layers. In Fig. 3, the FC layers (first sub-block of classification block) with 3 units (mentioned in small letter) corresponded to fc1, fc2 and fc3. Indeed, the three FC layers (fc1, fc2 and fc3) are used during model training while softmax classifier is employed. In such case, the last FC layer i.e. fc3 contains three neurons corresponding to three lens categories, and the softmax classifier is applied to this layer. However, model training requires the softmax classifier, due to which an additional fc layer consisting of three neurons, is added after second fc layer. Once the training is completed, this softmax layer (fc3) is replaced with the SVM classifier. Then, the SVM is trained on the features constituted by the second fc layer (fc2), and further used to map the given iris image to correct lens category. Here, it is worth noticing that in proposed DCLNet, the feature extractor part and SVM classifier is trained separately. Though, in our experiments SVM classifier exhibits better performance compared to Softmax, therefore, Multiclass SVM (with Radial Basis Function (RBF) kernel) is added as a classifier after second fc layer. The entire model design ends up with a total of 56 layers including Batch Normalization, ReLU and Dropouts. Moreover, since the initial layers are aimed to constitute basic features, such as points, edges, blobs, etc., they are not involved in training, while the remaining layers are finetuned. In our experiment, the initial 27 layers are kept freeze and rest 29 layers are retrained as shown in Fig. 3 on NIR iris images of contact lens datasets.

Fig. 4. Iris image augmentation. (a) Input image (b) Sheared image (c) Flipped image (d) left rotation (e) right rotation (f) flipping with shearing.

2.2. Image augmentation A deep convolution network requires a large number of images per subject to learn the discriminative feature representation. However, the iris datasets contain insufficient iris images per subject. Therefore, in order to generate supplementary training images, some augmentation techniques, such as shearing, flipping, rotation, rotation after shearing, shearing after a rotation at various angles and directions, etc., are carried out on input image matrix (the example is shown in Fig. 4). Such transformations are identical to the general matrix operations. Besides, the size/dimension of the input image also substantially influences the performance of CNN. This is due to the fact that the large image size creates more parameters that leads to overfitting [15,38]. Consequently, the image dimensions need to be down-sampled. In this view, the input image is first down-sampled to 224 × 224 (as the DenseNet model accepts input of size 224 × 224). Afterward, the Image Data Generator is incorporated to generate identical but pixel position variant images by performing transformations mentioned above. Indeed, the Image Data Generator is an inbuilt tool provided in keras library [39] to generate auxiliary images. However, it needs certain transformation parameters, such as rotation (≤40), shearing (≤0.2), flipping (horizontal and vertical as True), width shift (≤0.2), and height shift (≤0.2) has been considered that are associated with the image augmentation. Noticeably, the image augmentation is carried out merely

Fig. 5. Training and probe set generation through Image Data Generator.

for the training set (as depicted in Fig. 5), whereas the test set is created from the input dataset itself (as model testing does not require supplementary images). Afterward, the DCLNet model is fine-tuned on train set in order to learn the feature representation, then images in a probe set are classified by the SVM classifier. Here, it is worth noticing that the DCLNet model (in Fig. 5) represents the entire configuration (feature extractor part, training and the SVM classifier). 2.3. Feature extraction The overall performance of the proposed model depends on the quantity and quality of information contained in the input image matrix, as it is subsequently used for basic feature extraction. However, convolution does spatial dimensionality reduction and therefore input loss. To overcome this, zero paddings is applied around the boundary of the input image matrix. The length of the

Meenakshi C., Vivek T. and Venkanna U. / Future Generation Computer Systems 101 (2019) 1259–1270 Table 2 Learning parameters of DCLNet.

output feature map is given by (4) [36]. o=

(w − k + 2p) s

+1

(4)

where o = length of output feature map, w = input length, p = size of zero padding, k = filter size, and s = stride. Furthermore, each convolutional block (i.e. Conv_block) is a sequence of Batch normalization, Activation, and Convolution operations. Activation operation is supplemented through ReLU. The major benefit of Batch normalization is that it speeds up the learning process by normalizing the features of varying sizes. It takes a mini batch of features from the previous activation layer and normalizes them by subtracting each element by the Batch Mean and dividing by Standard Deviation as given in (7). Procedure: Batch Normalization (BN) (Applied to activation x over a mini-batch) Input: x over a mini-batch B Mini batch B = {x1 . . . xN }; Learning parameters: γ , β

µB ← σB ←

N 1 ∑

N

(5)

i=1

N 1 ∑

N

xi

(xi − µB )

(6)

i=1

(7)

yi ← yxˆ i + β ≡≡ BNy,B (xi )

(8)

σB2 + ∈

where µB and σB signify the mean and standard deviation of the mini batch. The first convolution layer uses a filter of 5 × 5 to learn more generic features. The convolution between input image and kernel is expressed as:

∑

Sr. No.

Parameters

Value

1 2 3 4 5

Optimizer Momentum Learning rate Batch size Epochs

SGD 0.9 0.0001 32 50

Average Pooling to 401408-dimensional vector to feed them in fc layers. The output of a fully connected layer l is denoted as

⎛ (l)

zj

⎞

M (l−1)

=σ

(l)

∑ ⎝

z

(l−1)

(l)

(l)

(i).w (i, j) + b (j)⎠

(11)

i=1 (l)

where Zj represents jth output of lth layer. M (l−1) is the term representing the number of neurons in (l − 1)th layer, w(l) (i,j) is the weight on connection between ith neuron n layer l − 1 and jth neuron of layer l, b(l) (j) is bias for neuron j in layer l, σ (l) is the activation for layer l. 2.4. Training strategy

2

xi − µ B xˆ i ← √

Yj =

1263

Xi ∗ wi,j + Bj

(9)

i

where Xi and Yj represent the ith input and the jth output feature map respectively. wi,j is the kernel convolving over the set of pixels i ∈ Xi i.e. ith input feature map. Bj denotes the bias for jth output feature map and * denotes convolution. The ReLU layer is used to impose non-linearity by using Y r j = max(0,Y j ). It converts all negative values in feature maps to zero. The Max-pool layer attempts to down-sample the convolved feature maps by applying max-pooling across 2 × 2 pixels with a stride of 2 while retaining same depth. The max-pooling layer is mathematically represented by the following expression [40]. z i j,k = max (Yij.p+m,k.p+n ) 0≤m,n
(10)

Here, Z i j,k represents a neuron in ith feature map, computed over a (p × p) local non-overlapping region in ith input feature map Y i j,k. As illustrated in Fig. 3, after the last dense block, a global average pooling layer is incorporated, which gives the spatial average of all feature maps at last convolutional layer. For an iris image, let fk (x,y) is the activation of kth unit, at spatial location (x,y) in the last convolution ∑ layer. Then, for unit k, the global average pooling results F k → x,y fk (x,y). Global Average Pooling at last convolution layer encourages the network to find the existence of correct lens pattern as opposed to Global Max-Pooling which is aimed to identify the most discriminative feature. This is owing to the fact that, on computing the average of the feature map, the ensuing value is maximized through the aggregation of all distinctive features of all lens categories. Next to the flatten layer is used to rearrange the output feature map from Global

For training our model, Stochastic Gradient Descent (SGD) optimizer with momentum is employed, as plain SGD can make erratic updates on non-smooth functions. SGD with momentum updates the weights with the moving average of the changes in individual weights for a single training sample. If everything is labeled with t then the moving average is given as:

∆Whl( ) (t) = η n

∑

(n)

(n)

deltal (t) + α.∆Whl (t − 1)

(12)

p

where second term α.∆W (n) (hl) (t − 1) is the momentum, which increases for dimensions where gradients point in the same direction and decrease for those with changing direction. Therefore, leads to fast convergence. Here, α is the momentum parameter, when α is zero, it works as simple SGD; whereas, small values of α fluctuates updating function to a great extent. If α increases, it adds a contribution from previous training samples [41]. In our experiment, α is set to 0.9, as large value turns out to have a smoother curve. In order to train feature extractor, the softmax loss function is used to calculate residuals which are backpropagated for weight adjustment in the early layers. After training feature extractor, the further task is to train the SVM classifier. To achieve this, the softmax classifier is removed and training features are extracted from the second fc layer. Afterward, these features are fed to the SVM classifier (with radial basis function or Gaussian kernel) that requires two important parameters, i.e. penalty term (C ) and error tolerance/tradeoff (γ ). C is basically a tradeoff between the training error and the generalization, i.e. the larger value of C tends to decrease the training error. However, it reduces the model generalization over testing data. Intuitively, γ defines how far the impact of a sole training sample reaches where the low values correspond to ‘far’ and high values denotes ‘close’ influence. In our experiments, the values for parameter C is 1.0 whereas, γ = 1/N ∗ v ar(x), where N and var(x) depict the number of features and variance of the given feature ‘x’. Such parameters result the best model performance. Furthermore, while model training, the following loss function is minimized [34]. The learning parameters used are listed in Table 2. Li =

∑ [max(0, wjT xi − wyTi xi + ∆)] j̸ =yi

(13)

1264

Meenakshi C., Vivek T. and Venkanna U. / Future Generation Computer Systems 101 (2019) 1259–1270

Network implementation description The implementation of the propose DCLNet has been carried out in python using Keras [39] library with Tensorflow [42] at the backend. The system configuration is as Intel Scalable Processors Xeon 4114, 64 GB DDR4 RAM, GTX 1080Ti 11 GB GPU card. 3. Experiments This section describes the experimental setup and result interpretation. All experiments are performed on IIIT Delhi (IIITD) Contact Lens and Notre Dame (ND) Iris datasets by following validation protocols. DCLNet is trained and tested on raw iris images (without segmentation and normalization) and CCR has been chosen as a primary measure for performance comparison. 3.1. Description of the dataset and validation protocols This subsection provides details about the datasets. Here, two publicly available databases are considered namely: ND Cosmetic Contact Lens 2013 database [43] and IIITD Contact Lens Iris Database [44]. The proposed architectures are trained and validated on these two datasets based on the given evaluation protocols. The detailed description of these datasets is given below. ND Database: The ND database is composed of two separate datasets ND-I and ND-II. ND-I contains images captured using IrisGuard AD100 [45] sensor. It is conceptually partitioned into training and probe sets of 600 and 300 images respectively. Whereas, images of ND-II were captured using LG4000 sensor [46]. It provides a training set of 3000 images and probe set of 1200 images. The cosmetic contact lenses in this database are manufactured by Jhonson and Jhonson [47], Ciba Vision [48] and Cooper Vision [49]. The proposed work has additionally been assessed by comparative analysis with state-of-the-art techniques by considering the evaluation protocols specified in the database. IIITD Database: It contains a total of 6570 iris images collected from 101 subjects; with Congent and Vista sensors are used for image acquisition. Since each subject contains left and right iris, collectively they form 202 unique iris instances. There are two manufacturers of Cosmetic lenses used in the dataset namely: CIBA Vision [48] and Bausch & Lomb [50]. The dataset is given with an evaluation protocol as 50 subjects should be used for training and rest 51 for testing the model performance. For validation, we follow the given protocol and compared with state-of-the-art. 3.2. Experimental outcomes In this subsection, several experiments are executed on the aforementioned datasets with three validation strategies i.e. intra-sensor validation, inter-sensor validation, and multi-sensor validation. i. Intra-sensor validation Training and validation performed on images captured from the same sensor are termed as intra-sensor validation. Table 3 and Fig. 6 depict the performance comparison of proposed DCLNet with existing methods such as; Deep Image Representation (DIR) [29], ContlensNet [30] and GHCLNet [31]. As both datasets have two sensors, the table contains a total of four experiments for each. These experiments calculate CCR for individual lens category, where C–C represents ‘‘Colored-lens to Colored-lens’’, N–N is ‘‘No-lens to No-lens’’ and S–S is ‘‘Soft-lens to Soft-lens’’ classification. Final result (CCR %) is obtained by averaging individual CCRs

Table 3 Model performance (In CCR %) for intra-sensor validation. Data base

Classification DIR [29]

ContlensNet [30]

GHCLNet [31] DCLNet

IIITDCongent

C–C N–N S–S

73.00 35.50 98.21

100 68.68 93.62

100 89.86 91.26

99.10 94.19 92.33

Average

69.05

86.73

93.71

95.20

IIITD-Vista

C–C N–N S–S

55.88 60.80 98.30

100 74.50 87.50

100 94.6 91.88

100 93.19 92.89

Average

72.08

87.33

95.49

95.36

ND-I

C–C N–N S–S

99.75 84.50 73.75

100 93.25 97.50

100 91.67 87.50

98.50 89.49 90.86

Average

86.00

96.91

93.05

92.95

ND-II

C–C N–N S–S

97.00 73.00 65.00

100 88.00 97.00

99.75 95.24 89.74

99.93 92.86 94.45

Average

78.33

95.00

94.91

95.74

Fig. 6. DCLNet performance comparison with state of the arts for intra-sensor validation.

Some key observations figure out from results are; with IIITDCongent sensor, the proposed DCLNet exhibits average CCR of 95.20% with a minimum hike of 2% then all state of the arts. With Vista Sensor, DCLNet performs better than DIR and ContlensNet [30] and comparable to GHCLNet [31] with 95.36% CCR. For ND-I database DCLNet achieves average CCR % of 92.95, which is less than state-of-the-art. This is due to less number of images available in ND-I dataset for training. However, with ND-II, the model gives 95.74% of CCR, which is best among all state of the art models. ii. Inter-sensor validation Inter-sensor validation is performed on a pair of sensors, i.e. it requires the model to be trained on images of one sensor and tested on images with a different sensor. With IIITD dataset, DCLNet model is trained and tested pair-wise among Congent and Vista sensors. Whereas, with ND dataset, the experiment is done on a pair of ND-I and ND-II. Consequently, four different cases occur as depicted in Table 4 which indicates the performance of the proposed model for different sensor pairs. Fig. 7 represents DCLNet performance comparison with state-of-the-art. Some key observations inferred from the aforementioned results are; when DCLNet is trained and tested on Congent and Vista images respectively, it underperforms with respect to ContlensNet [30] and GHCLNet [31]. However, when Vista dataset is

Meenakshi C., Vivek T. and Venkanna U. / Future Generation Computer Systems 101 (2019) 1259–1270

1265

Table 4 Model performance (in CCR %) for inter-sensor validation. Exp. Train database

1

2

3

4

IIITDCongent

IIITDVista

ND-I

ND-II

Test database

Classification type

DIR [29]

ContlensNet [30]

GHCLNet [31]

DCLNet

IIITDVista

C–C N–N S–S

89.61 06.00 45.47

100 96.19 88.23

99.25 93.40 83.37

99.83 89.55 81.74

IIITDCongent

ND-II

ND-I

Average

45.51

94.80

92.01

90.37

C–C N–N S–S

38.15 48.67 42.25

78.91 87.75 87.75

85.36 96.74 65.73

99.82 81.43 79.26

Average

43.08

84.80

82.61

86.83

C–C N–N S–S

94.00 75.00 65.00

97.50 68.50 98.00

100 81.25 93.27

97.92 83.00 92.90

Average

78.00

88.00

91.51

91.27

C–C N–N S–S

97.00 80.00 49.00

100 81.33 90.03

98.00 91.90 81.84

100 92.00 84.34

Average

75.33

90.45

90.58

92.11

Table 5 Model performance (in CCR %) for multi-sensor validation. Database

Classification Type

DIR [29]

ContlensNet [30]

GHCLNet [31]

DCLNet

IIITDCombined

C–C N–N S–S

61.07 47.55 97.99

98.50 96.56 88.90

99.73 91.87 92.85

99.87 92.82 92.10

NDCombined

Average

69.28

94.65

94.82

94.93

C–C N–N S–S

99.60 77.40 71.40

100 95.40 82.40

100 91.67 95.04

99.93 93.89 94.32

Average

82.80

92.60

95.57

96.04

Fig. 7. DCLNet performance comparison with state of the arts for inter-sensor validation.

used for training and Congent for testing, DCLNet achieves the best performance with a minimum hike of more than 2% in CCR. For ND dataset, when training and testing are performed on ND-I and ND-II respectively, DCLNet performs better than DIR [29] and ContlensNet [30] and comparable to GHCLNet [31] with 91.27% accuracy. Similar results are observed when the model is trained and tested on ND-II and ND-I respectively, i.e. hike of 2%. iii. Multi-sensor validation For multi-sensor validation, images from heterogeneous sensors are combined and stored in a single database. Such database is referred to as the multi-sensor dataset and corresponding validation is termed as multi-sensor validation. In this view, Congent and Vista sensor images are combined to form IIITDCombined dataset. Similarly, ND-I and ND-II sensor images are merged to constitute ND-Combined dataset. The average reported CCR is 95.36%. Table 5 and Fig. 8 represent the DCLNet performance comparison with state of the arts for multi-sensor validation. Some key observations are inferred from results that; with IIITD-Combined dataset, the performance of DCLNet is reported as 94.93%, which outperforms all state of the arts. With ND-combined dataset, DCLNet performs best among all state of the arts with 96.04% CCR.

Fig. 8. DCLNet performance comparison for multi-sensor validation.

3.3. Analyzing discrimination power of DCLNet The performance analysis of the proposed DCLNet described in the previous subsection was based on CCR%. However, CCR alone is not enough and robust metric to assess a classifier, as it does not consider the class imbalance and underlying feature distribution. In addition, the accuracy depends on some bias/threshold value and varies according to variation in the threshold, which happens due to its insensitivity to underlying class distribution. Therefore, proposed DCLNet is additionally assessed against more

1266

Meenakshi C., Vivek T. and Venkanna U. / Future Generation Computer Systems 101 (2019) 1259–1270

robust and reliable measure i.e. ROC, which represents the comparison between True Positive Rate (TPR) and False Positive Rate (FPR) returned by the classifier based on each possible threshold value. Furthermore, another useful metric ‘‘Area under the Curve (AUC)’’ is employed for model evaluation, which measures the performance of DCLNet across all possible thresholds. In this section, the discriminating power of DCLNet among three lens categories has been estimated through ROC curves. Here, we have plot three best ROC curves for inter-sensor, intrasensor, and multi-sensor validation respectively. The ROC plot is shown in Fig. 9(a) represents the performance of DCLNet for Intra-sensor validation on IIITD-Vista sensor. It performs accurate prediction for Colored, No Lens and Soft Lens with AUC = 1, 1 and 0.98 respectively. Its best performance on Vista is due to high-quality images with clearer iris texture. The ROC plot depicted in Fig. 9(b) is constructed for DCLNet while it is trained and tested on ND-II and ND-I sensor respectively, where classes 0, 1, 2 represent Colored lens, No lens, and Soft lens respectively. The model’s performance is considerably impressive for Colored and No Lens categories with higher AUC of 0.99 and 0.97 respectively. However, slightly less AUC of 0.81 has been observed for Soft lens. The exploration of SVM outcomes for soft lens images depicts that it labels the soft lens image features with no lens category. This is probably due to the poor quality of iris images contained in the ND dataset that causes difficulty in discriminating between No lens and Soft lens. As, it can be observed that along with the proposed DCLNet, all the state of the art methods exhibiting degraded performance, however, our model is still performing superior. In addition, the ND-II dataset contains fewer images for model training, which reasons insufficient samples to extract the discriminating features. This may be another cause behind the performance degradation. Fig. 9(c) depicts the ROC plot for DCLNet while it was trained and tested on two different sets of images combined from both Vista and Congent sensor where, classes 1, 2, 3 represent Colored lens, No lens and Soft Lens respectively. The model is accurately predicting Colored lens with AUC = 1 and also exhibits good prediction for No lens and Soft lens images with AUC = 0.97. 3.4. Impact of normalization on DCLNet performance This subsection analyses the impact of normalization on DCLNet performance. First, the proposed network is trained on iris images without normalization and the results are observed. Next, the images from both datasets are normalized by using an open source tool Osiris v4.1 [51]. It segments the input iris image by localizing iris and pupillary boundaries yielded by center coordinates along with radius of iris and pupil, using following integrodifferential operator [1].

⏐ ⏐

max ⏐⏐Gσ (r) ∗

(r ,x0 ,y0 )

⏐∫ I (x, y) ∂ ⏐⏐ ds ∂ r ⏐ r ,x0 ,y0 2π r

(14)

It convolves over image domain I(x,y) to search for the maximum in the partial derivative with an increase in radius ‘r’ of the integral of I(x,y) corresponding to circular arc ds with center (x0 ,y0 ) and radius ‘r’. The * operator and Gσ (r) denote convolution and the smoothing function respectively. Next, the iris region is normalized to polar coordinates using Daugman’s Rubber sheet model [1]. It maps the iris image I(x,y) from Cartesian coordinates to polar coordinates (r, θ ) by using I(x(r , θ ), y(r , θ )) → I(r , θ )

(15)

where x (r, θ ) and y (r, θ ) are the scaler combination of both the set of pupillary boundary points (xp (θ ), yp (θ )) and the set of

Table 6 Model performance (in CCR %) with and without normalization for intra-sensor validation. Data base

IIITDCongent

IIITD-Vista

ND-I

ND-II

Classification type

DCLNet (with normalization)

DCLNet (without Normalization)

C–C N–N S–S

98.72 90.50 90.05

99.10 94.19 92.33

Average

93.09

95.20

C–C N–N S–S

100 89.90 89.10

100 93.19 92.89

Average

93.00

95.36

C–C N–N S–S

96.24 86.90 87.23

98.50 89.49 90.86

Average

90.12

92.95

C–C N–N S–S

99.29 90.65 90.82

99.93 92.86 94.45

Average

93.58

95.74

limbus boundary points (xq (θ ), yq (θ )) with outer perimeter of iris. Both these are detected from the maximum of (14) as x(r , θ ) = (1 − r)xp (θ ) + rxq (θ ) y(r, θ ) = (1 − r)yp (θ ) + ryq (θ )

(16)

Finally, the network is trained and tested on normalized images and validated on three benchmarks i.e. intra-sensor validation, inter-sensor validation, and combined-sensor validation. i. Intra-sensor validation Further experiments have been carried out to observe the impact of iris normalization on the performance of DCLNet for intra-sensor validation. In this, the proposed DCLNet is trained and tested on iris images from both versions of datasets (normalized dataset and raw dataset). Table 6 describes the results obtained as; with IIITD database, normalization causes degradation in accuracy approximately by 2% for both Congent as well as Vista sensors. Similarly, more than 2% accuracy deduction is reported for ND-I and ND-II sensors. It can be inferred from results that normalizing iris images do not improve the model’s accuracy. In fact, it causes significant degradation due to flaws in the underlying normalization technique. For instance, according to Daugman’s [11] assumption, the pupil is always circular, which is not always true, as it may have an elliptic shape. Consequently, the segmentation and normalization are not accurate and therefore, overall accuracy suffers from this flaw. ii. Inter-sensor validation Next experiment is carried out to analyze the impact of normalization on the model’s performance for inter-sensor validation. In this regard, the images from both databases are normalized, then DCLNet is trained and tested between a pair of different sensors. Table 7 shows the proposed model performance achieved on inter-sensor validation. With IIITD database, normalization exhibits performance degradation, when a model is trained on Congent and tested on Vista images. Whereas, it gives a hike of 4% in vice-versa case. Similarly, for normalization degrades accuracy by 2%. iii. Multi-sensor validation Further experiments are carried out to analyze the impact of normalization on DCLNet performance for multi-sensor validation. Experimental results in Table 8 show that, with IIITDCombined dataset, a degradation of more than 7% is reported in accuracy while training and testing DCLNet on normalized images. Similarly, with ND-Combined dataset, CCR is reduced by 2.5%.

Meenakshi C., Vivek T. and Venkanna U. / Future Generation Computer Systems 101 (2019) 1259–1270

1267

Fig. 9. ROC curves of DCLNet (a) intra-sensor validation over IIITD Vista (b) inter-sensor validation on ND-II and ND-I (c) Multi-sensor validation over IIITD Dataset. Table 7 Model performance (in CCR %) with and without normalization for inter-sensor validation. Train database

Test database

Classification type

DCLNet (Normalized)

DCLNet (Not Normalized)

IIITDCongent

IIITDVista

C–C N–N S–S

99.81 82.97 76.38

99.83 89.55 81.74

IIITD-Vista

ND-I

ND-II

IIITDCongent

ND-II

ND-I

Average

86.83

90.37

C–C N–N S–S

96.89 86.46 93.54

99.82 81.43 79.26

Average

92.29

86.83

C–C N–N S–S

97.52 80.48 94.69

97.92 83.00 92.90

Average

90.89

91.27

C–C N–N S–S

93.67 87.92 88.00

100 92.00 84.34

Average

89.86

92.11

Fig. 10(a). Intra-sensor validation for IIITD Congent sensor (Colored).

Table 8 Model performance (in CCR %) with and without normalization for multi-sensor validation. Database

Classification type

DCLNet (Normalized)

DCLNet (NotNormalized)

IIITDCombined

C–C N–N S–S

97.75 76.07 72.70

99.87 92.82 92.10

Average

86.17

94.93

C–C N–N S–S

96.66 94.67 89.43

99.93 93.89 94.32

Average

93.58

96.04

NDCombined

It can be observed that normalization causes performance degradation in majority of cases. This is due to the fact that the technique used for normalizing iris images itself has some drawbacks; such as it is affected by surrounding factors i.e. eyelashes, sclera, etc. On the other hand, the pupil size is not identical for all individuals. Due to such issues, the normalization operation loses some essential features and therefore degrades performance. 3.5. Performance analysis The foremost observation inferred from the experimental analysis is that for proposed DCLNet model, SVM performs better classification compared to Softmax. In this regard, we prepare two versions of proposed DCLNet model i.e. (i) DCLNet with Softmax and (ii) DCLNet with SVM. Experiments on both versions

Fig. 10(b). Inter-sensor validation on Vista as training and Congent as testing dataset (No-lens).

are set up with validation schemes mentioned in Section 3.1. Table 9 and Figs. 10(a)–10(d) represent few experimental results produced by DCLNet with Softmax and compare it against DCLNet with SVM in the form of CCR. Fig. 10(a) shows the Intra-sensor validation accuracy of 98.5% for Colored lens on IIITD Congent sensor. Whereas, SVM classifier reports 99.10%. Fig. 10(b) represents 80% accuracy for Inter-sensor validation with Vista as training and Congent as a testing dataset for (N–N ) classification, however, with SVM it was 81.43%. Fig. 10(c) reports CCR of 82% for (S–S) classification, when ND-II dataset is used for training and ND-I is used for testing; whereas SVM gives 84.34% of CCR. The next experiment was carried out for multi-sensor validation on IIITD-Combined dataset with (S–S) classification as in Fig. 10(d), where softmax report 91.21% accuracy, while SVM gives 92.10% CCR. With the aforementioned results, it can easily be inferred that SVM classifier exhibiting better performance than Softmax with same iris features. Therefore, we have employed SVM for classification among three lens categories.

1268

Meenakshi C., Vivek T. and Venkanna U. / Future Generation Computer Systems 101 (2019) 1259–1270 Table 9 Performance comparison between softmax and SVM classifier in DCLNet model. Sr. No.

Experiments

DCLNet with Softmax

DCLNet with SVM

1 2

Intra-sensor validation for Colored Lens on IIITD Congent sensor Inter-sensor validation as Vista as training and Congent as testing dataset for (N–N ) classification ND-II dataset is used for training and ND-I is used for testing for (S–S) classification Multi-sensor validation for (S–S) classification on IIITD dataset

98.5% 80%

99.10% 81.43%

82%

84.34%

91.21%

92.10%

3 4

Fig. 10(c). Inter-sensor validation with ND-II as training and ND-I as testing dataset (Soft Lens).

expensive still achieving comparable, in fact, better performance without iris segmentation and normalization. Though, it employed some less expensive image augmentation techniques, to produce more number of similar images, so that the network learns to constitute more refined features. GHCLNet [31] offers complex architecture with two identical ResNet50 models with more than 170 layers each, both works in combination. The model gives better performance in majority cases, however, handling such huge network is much complicated. The proposed DCLNet can achieve similar performance with fewer layers and learning parameters. It has been observed in Sub-section D that, normalization causes performance degradation. In intra-sensor validation, the proposed DCLNet performs better with un-normalized data. Almost similar results have been observed with inter-sensor and multi-sensor validation. Based on these observations, it can be inferred that, while using deep CNN models, iris normalization is not desired. More specifically, deep CNN models can perform better without iris normalization. 3.7. Individual layer features visualization and analysis

Fig. 10(d). Multi-sensor validation for IIITD-Combined (Soft Lens).

3.6. Results and discussion To the best of our knowledge, ContlensNet [30] and GHCLNet [31] are two recent research developments, available with deep convolutional architectures for contact lens detection. The experimental results show that the performance of the proposed model is better or at least comparable with the state of the arts. In Intra-sensor validation, DCLNet gives the best accuracy in majority cases. In inter-sensor validation, the individual classification accuracy of the proposed model lacks for some classes, yet the average CCR is higher or equal to the state of the arts. For multi-sensor validation, DCLNet exhibits the best performance. Nevertheless, our significant contribution is that the DCLNet is a less complex model with optimal layer configuration, containing fewer learning parameters while exhibiting comparable performance with state of the arts. ContlensNet [30] used iris normalization using OSIRIS V4.1 tool, which exhibits limited performance due to some factors such as illumination, occlusion, and opposite acquisition environment. It is clear from the sub-section D that normalization causes performance degradation. Moreover, it requires additional efforts to generate patches of size 32 ∗ 32 from normalized images as training patterns. The proposed model is less computationally

Fig. 11 visualizes the features learned by different convolutional layers of the proposed DCLNet. It is observed for each lens category that initial layers (9th and 16th convolutional layers) learn more general features i.e. dark pixels, blobs, points, etc. and generate 128 feature maps of size (56 ∗ 56). At the next level, i.e. 19th convolutional layer seeks for edges and corners with 32 feature maps of the same size. The 33rd convolutional layer plays a major role in separating the iris region from the sclera. Here, it is easy to interpret that since convolutional layers are capable enough to find deeper features in the images, therefore iris segmentation and normalization are not required. Consequently, by moving deeper in the network, the model learns more specific features corresponding to three lens categories. Finally, the 47th convolutional layer learns iris textual patterns. 4. Conclusion Contact lens detection is an eminent issue in the realm of iris recognition. Since the contact lens dissembles the actual iris texture and can possibly be exploited to forge the iris recognition system, several techniques were proposed in the literature to address such problem. The conventional techniques employed handcrafted iris features and achieved satisfiable results. However, recently deep convolutional networks have been employed to accomplish this task. DCLNet is a densely connected convolutional network with less number of layers and fewer learning parameters. Due to the dense connections between layers, it learns more crucial features. Moreover, it does not require iris segmentation and normalization. Further, for performance evaluation, comprehensive experiments were conducted on two generalized databases with three diverse evaluation strategies. The qualitative results represent that the proposed model obtains comparable results with state of the arts and even better in some experiments. Besides, the paper also attempts to analyze

Meenakshi C., Vivek T. and Venkanna U. / Future Generation Computer Systems 101 (2019) 1259–1270

1269

Fig. 11. Layer specific visualization of iris features.

the impact of iris normalization on accuracy reported by DCLNet. Comparative results show that normalization causes degradation in the model’s accuracy in majority cases. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References [1] J. Daugman, How iris recognition works, IEEE Trans. Circuits Syst. Video Technol. 14 (1) (2004) 21–30. [2] Gad Ramadan, Mohammed Talha, Ahmed A. Abd El-Latif, M. Zorkany, ELSayed Ayman, EL-Fishawy Nawal, Ghulam Muhammad, Iris recognition using multi-algorithmic approaches for cognitive internet of things (CIoT), Future Gener. Comput. Syst. (2018) Elsevier. [3] https://uidai.gov.in/, Unique Identification Authority of India. [4] https://www.amsterdam-airport.com/, Amsterdam Airport Schiphol (AMS). [5] https://www.cbsa-asfc.gc.ca, Canada Border Services Agency. [6] J. Daugman, High confidence visual recognition of persons by a test of statistical independence, IEEE Trans. Pattern Anal. Mach. Intell. 15 (11) (1993) 1148–1161. [7] K. Hollingsw, K. Bowyer, P.J. Flynn, Pupil dilation degrades iris biometric performance, Comput. Vis. Image Underst. 113 (2009) 150–157. [8] S.S. Arora, M. Vatsa, R. Singh, A.K. Jain, On Iris camera interoperability, in: IEEE 5th International Conference on BTAS, 2012, pp. 346–352. [9] K.W. Bowyer, S.E. Baker, A. Hentz, K. Hollingsworth, T. Peters, P.J. Flynn, Factors that degrade the match distribution in iris biometrics, Springer Identity Inf. Soc. 2 (3) (2009) 327–343. [10] Yingxue Wang, Yanan Chen, Md Zakirul Alam Bhuiyan, Yu Han, Shenghui Zhao, Jianxin Li, Gait-based human identification using acoustic sensor and deep neural network, Future Gener. Comput. Syst. 86 (2018) 1228–1237, Elsevier. [11] J. Daugman, New methods in iris recognition, IEEE Trans. Syst. Man Cybern. B 37 (5) (2007) 1167–1175. [12] D.M. Monro, S. Rakshit, D. Zhang, DCT-based iris recognition, IEEE Trans. Pattern Anal. Mach. Intell. 29 (4) (2007) 586–595. [13] K. Miyazawa, K. Ito, T. Aoki, K. Kobayashi, H. Nakajima, An effective approach for iris recognition using phase-based image matching, IEEE Trans. Pattern Anal. Mach. Intell. 30 (10) (2008) 1741–1756. [14] J.K. Pillai, V.M. Patel, R. Chellappa, N.K. Ratha, Secure and robust iris recognition using random projections and sparse representations, IEEE Trans. Pattern Anal. Mach. Intell. 33 (9) (2011) 1877–1893. [15] H. Proença, L.A. Alexandre, Toward covert iris biometric recognition: Experimental results from the NICE contests, IEEE Trans. Inf. Forensics Secur. 7 (2) (2012) 798–808. [16] D. Yadav, N. Kohli, J.S. Doyle, R. Singh, M. Vatsa, K.W. Bowyer, Unraveling the effect of textured contact lenses on iris recognition, IEEE Trans. Inf. Forensics Secur. 9 (5) (2014) 851–862.

[17] J. Daugman, Demodulation by complex-valued wavelets for stochastic pattern recognition, Int. J. Wavelets Multiresolut. Inf. Process. 01 (01) (2003) 1–17. [18] E.C. Lee, K.R. Park, J. Kim, Fake iris detection by using purkinje image, in: Proceedings of the International Conference on Advances on Biometrics (ICB ’06), in: Lecture Notes in Computer Science, vol. 3832, Springer, Hong Kong, 2006, pp. 397–403. [19] X. He, S. An, P. Shi, Statistical texture analysis-based approach for fake iris detection using support vector machines, in: Proceedings of the International Conference on Advances on Biometrics (ICB ’07), in: Lecture Notes in Computer Science, vol. 4642, Springer, Berlin, Heidelberg, 2007. [20] Z. Wei, X. Qiu, Z. Sun, T. Tan, Counterfeit iris detection based on texture analysis, in: IEEE 19Th International Conference on Pattern Recognition, Tampa, FL, 2008, pp. 1–4. [21] Z. He, Z. Sun, T. Tan, Z. Wei, Efficient iris spoof detection via boosted local binary patterns, in: Advances in Biometrics (ICB 2009), in: Lecture Notes in Computer Science, vol. 5558, Springer, Berlin, Heidelberg, 2009. [22] H. Zhang, Z. Sun, T. Tan, Contact lens detection based on weighted LBP, in: IEEE 20Th International Conference on Pattern Recognition, Istanbul, 2010, pp. 4279–4282. [23] D. Zhao, W. Luo, R. Liu, L. Yue, Negative iris recognition, IEEE Trans. Dependable Secure Comput. 15 (1) (2018) 112–125, (negative database design). [24] S.S. Barpanda, P.K. Sa, O. Marques, B. Majhi, S. Bakshi, Iris recognition with tunable filter bank based feature, Springer 77 (6) (2018) 7637–7674, https://link.springer.com/journal/11042/77/6/page/1. [25] N.P. Ramaiah, A. Kumar, Towards more accurate iris recognition using cross-spectral matching, IEEE Trans. Image Process. 26 (2017) 208–221. [26] M. Trokielewicz, E. Bartuzi, Cross-spectral iris recognition for mobile applications using high-quality color images, J. Telecommun. Inf. Technol. (3) (2018) 91–97. [27] R. Raghavendra, K.B. Raja, Ch. Busch, Ensemble of statistically independent filters for robust contact lens detection in iris images, in: ACM Proceding of Indian Conference on Computer Vision Graphics and Image Processing, (ICVGIP 2014), 2014, 04. [28] K. Okokpujie, E. Noma-Osaghae, S. John, A. Ajulibe, An improved iris segmentation technique using circular hough transform, in: K. Kim, H. Kim, N. Baek (Eds.), IT Convergence and Security 2017, in: Lecture Notes in Electrical Engineering, vol. 450, Springer, Singapore, 2018. [29] P. Silva, E. Luz, R. Baeta, H. Pedrini, A. Falcao, D. Menotti, An approach to iris contact lens detection based on deep image representations, in: IEEE 28Th SIBGRAPI Conference on Graphics, Patterns and Images, Salvador, 2015, pp. 157–164. [30] R. Raghavendra, K.B. Raja, C. Busch, ContlensNet: Robust iris contact lens detection using deep convolutional neural networks, in: IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA, 2017, pp. 1160–1167. [31] A. Singh, V. Mistry, D. Yadav, A. Nigam, GHCLNet: A generalized hierarchically tuned contact lens detection network, in: IEEE 4Th International Conference on Identity, Security, and Behavior Analysis (ISBA), 2018, 978-1-5386-2248.

1270

Meenakshi C., Vivek T. and Venkanna U. / Future Generation Computer Systems 101 (2019) 1259–1270

[32] H. Qin, M.A. El-Yacoubi, Deep representation-based feature extraction and recovering for finger-vein verification, IEEE Trans. Inf. Forensics Secur. 12 (8) (2017) 1816–1829. [33] H. Proenc¸ a, J. Neves, Deep-PRWIS: periocular recognition without the iris and sclera using deep learning frameworks, IEEE Trans. Inf. Forensics Secur. 13 (4) (2018) 888–896. [34] Benıtez-Pena Sandra, Rafael Blanquero, Emilio Carrizosa, Pepa RamırezCobo, On Support Vector Machines under a multiple-cost scenario. working Paper, Springer, 2017. [35] D. Menotti, et al., Deep representations for iris, face, and fingerprint spoofing detection, IEEE Trans. Inf. Forensics Secur. 10 (4) (2015) 864–879. [36] G. Huang, Z. Liu, L. v. d. Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp. 2261–2269. [37] ImageNet Large Scale Visual Recognition Challenge (ILSVRC) Available: http://image-net.org/challenges/LSVRC/. [38] Liu M. Zhang, H. Li, Z. Sun, T. Tan, Deepiris: Learning pairwise filter bank for heterogeneous iris verification, Pattern Recognit. Lett. 82 (2) (2015) 154–161. [39] F. Chollet, et al., Keras. https://github.com/fchollet/keras, 2015. [40] D. Scherer, A. Muller, S. Behnke, Evaluation of pooling operations in convolutional architectures for object recognition, in: International Conference on Artificial Neural Networks (ICANN), in: Lecture Notes in Computer Science, vol. 6354, Springer, Berlin, Heidelberg, 2010. [41] L. Bottou, Large-scale machine learning with stochastic gradient descent, in: Proceedings of COMPSTAT 2010, Physica-Verlag HD, 2010. [42] A. Mart, et al., Tensorflow: Large-scale machine learning on heterogeneous systems. https://www.tenssorflow.org. [43] The ND Contact Lens Database 2013 is available upon request. Access information can be found at http://www3.nd.edu/cvrl/CVRL/Data_Sets. html. [44] The IIIT-Delhi Contact Lens Iris Database is available upon request. Access information can be found at https://research.iiitd.edu.in/groups/iab/ irisdatabases.html. [45] Irisguard, washington, DC, USA, ad100 camera, Available: http://www. Irisguard.com/uploads/AD100ProductSheet.pdf, 2013. [46] Lg, riyadh, saudi arabia, LG 4000 camera, Available: http://www.lgIris.com/, 2011. [47] Johnson and johnson, skillman, NJ, USA, acuvue2 colours, Available: http: //www.acuvue.com/products-acuvue-2-colours, 2013. [48] Cibavision, and duluth, GA, USA, freshlook colorblends, Available: http: //www.freshlookcontacts.com/, 2013. [49] C. vision, expressions colors, Available: http://coopervision.com/contactlenses/expressions-color-contacts, 2013. [50] Bausch, R. Lombster, NY, USA, bausch & lomb lenses, Available: http: //www.bausch.com/, 2014. [51] D. Petrovska, A. Mayoue, Description and documentation of the biosecure software library, technical report, BioSecure, Project No IST-(2007) 2002-507634. Available: https://biosecure.wp.tem-tsp.eu/evaluationframework/.

Meenakshi Choudhary received the B.Tech degree in Computer Science Engineering from Rajiv Gandhi Technical University, Bhopal, in 2012, and M.Tech degree from Rajiv Gandhi Technical University, Bhopal. She is currently working toward the Ph.D. degree from IIIT Naya Raipur, India. Her current research interest includes Biometric Pattern Recognition, Deep Learning, Image Processing, and Computer Vision.

Vivek Tiwari is an Assistant Professor in the Department of Computer Science and Engineering at Nationally renowned Government International Institute of Information Technology (IIIT), Naya Raipur, C. G. (India). Earlier, he worked with the Mody Institute of Technology and Science (MITS), Deemed University, Sikar, Rajasthan and Caresoft Incorporation (based at Middlesex, NJ, USA). He is the recipient of Young Scientist Fellowship (MPYSC_2014_814) for the year 2014–2016 by the MPCST (Madhya Pradesh Council of Science & Technology), Govt. of M.P. He has handled an academic research project of 3.5 lac funded by IIT Bombay and MHRD under NMEICT mission. His broad research interest areas include Data Mining, Data Warehousing, Business Analytics, Machine learning, Predictive analytics.

Venkanna U. obtained his Ph.D. degree by the National Institute of Technology, Tiruchirappalli (NITT), in 2015. Since 2005, he has been in the teaching profession and currently he is an Assistant Professor in the Department of Computer Science and Engineering, Dr. Shyama Prasad Mukherjee International Institute of Information Technology, Naya Raipur (IIIT- NR). He has eight years of teaching experience and five years of research experience. His research interests include Internet of Things (IoT), Software Defined Networks, Network Security, Wireless Ad hoc, and Sensor network. He has to his credit of publishing 11 research papers including 3 in International Journals (SCI Indexed) and 7 in International Conferences.