Using brain prints as new biometric feature for human recognition

Accepted Manuscript

Using Brain Prints as New Biometric Feature for Human Recognition Kamel Aloui , Amine Nait-Ali , Mohamed Saber Naceur PII: DOI: Reference:

S0167-8655(17)30363-X 10.1016/j.patrec.2017.10.001 PATREC 6955

To appear in:

Pattern Recognition Letters

Received date: Revised date: Accepted date:

6 January 2017 8 September 2017 2 October 2017

Please cite this article as: Kamel Aloui , Amine Nait-Ali , Mohamed Saber Naceur , Using Brain Prints as New Biometric Feature for Human Recognition , Pattern Recognition Letters (2017), doi: 10.1016/j.patrec.2017.10.001

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Highlights

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A Brain-print based biometric approach is a proposed as a modality to recognize individuals. Brain folds and the sulco-gyral patterns are specific to each individual. Representation of brain folds using curvilinear allows low variability detection. Brain-print similarities can be used for age and gender estimation in forensic applications.

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Pattern Recognition Letters journal homepage: www.elsevier.com

Using Brain Prints as New Biometric Feature for Human Recognition Kamel Alouia, Amine Nait-Aliband Mohamed Saber Naceura a

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University of Tunis El Manar, National Engineering School (ENT), LTSIRS Laboratory, BP 37 Le Bélvédère, 1002 Tunis, Tunisia b University Paris-Est Créteil (UPEC), LiSSi, EA 3956, 122 rue Paul Armangot, 94400 Vitry sur Seine, France

ABSTRACT

2012 Elsevier Ltd. All rights reserved.

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Considering the evolution of neuroimaging in the medical field, some new emerging biometric modalities become interesting and promising candidates to recognize persons. These modalities are considered as a part of “`Hidden Biometrics” which consists in using clinical measurements and medical imaging for recognition purpose. The main motivation in using hidden biometrics is the fact that system attacks may be extremely difficult to consider. This specificity highly contributes in increasing the robustness in terms of person verification and identification. In this paper, we deal with a novel non-invasive approach to recognize persons by extracting a brain signature, called “brainprint”. In particular, we explored the brain cortical regions of volumetric brain MRI (Magnetic Resonance Imaging) images, acquired from 220 healthy subjects. For each subject, four 3D cortical surfaces are considered, then transformed into 2D cortical folds maps. From the resulting textures, brainprints are constructed by extracting features using Wavelet Gabor Transform. These brainprints are considered in this work as a discriminative signature of the brain. In terms of performance evaluation, we show that an EER=2.90±0.47 is reached for verification mode. On the other hand, when dealing with identification, the proposed approach allows a recognition rate of 99.6%.

Correspondingauthor. Amine Nait-Ali, University Paris-Est Créteil (UPEC), LiSSi, EA 3956,122 rue Paul Armangot, 94400 Vitry sur Seine, France, 86-300-8008; fax: +0-000-000-0000; [email protected]

Tel.: +33-

ACCEPTED MANUSCRIPT sulci. The gyri and sulci called sulco-gyral patterns form the brainprint. The growth of the brain in the skull provides a unique signature. In this context, many theories have addressed the issue of brain folds origin (Van Essen, 1997). The arguments were classified schematically into two types: 1. The first theory considers that only micro external mechanical forces form the brain folds. Indeed, the brain as a resilient organ expands within the skull which is rigid organ and limited in size. The resulting mechanical tensions allow stationary, stable and unique sulco-gyral patterns. 2. The second theory assumes the existence of a relationship between the morphology and the final structure of the brain, on one hand, and its cyto-architectonic and functional organization, on the other hand. One can conclude that both mechanical tension (occuring during brain growth) and experience that we learn allow to the cortical and sub-cortical patterns to develop in distinctly different ways (Régis et al., 2005; Van Essen, 1997). This shows that brain folds are discernibly different among individuals. Furthermore, recent studies in genetic and neuroscience show that the jumping genes, which provide identical twins being different, may also influence the brain folds and shape (Gage & Muotri, 2012). All these studies show that the human brain morphology is unique. Moreover, studies of brain morphometric and some work on the cerebral asymmetry have shown that even a number of brain sulci (main sulci) are present in all individuals. These differ in their positions, shapes and numbers of components. Indeed, the same sulcus met in several individuals may be more or less profound, more or less long, more or less sinuous and can also be broken and composed of multiple distinct furrows (Renault, 2001). Although, both right and left hemispheres are very similar in size, shape and weight, but the distribution of brain tissues (white and gray matter) and the outline of brain folds are not the same and are different from one hemisphere to the other. Hence, both hemispheres of the same individual are not symmetrical. Thus, two brains will never have the same aspect, even in identical twins (Glick, et al. 1982; Tucker & Williamson, 1984; Thompson, et al., 1996). Consequently, brain folds and the sulco-gyral patterns are specific to each individual, which makes all human brains, anatomically different.

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Nowadays, person’s identification and verification are increasingly used in different fields and different applications depending to the required level of security (Jain, et al. 2006; Maltoni, et al. 2009; Nait-Ali & Fournier, 2012). Among the most commonly used biometric modalities, one can obviously mention fingerprints (Chen, et al. 2013; Feng, et al. 2006), palmprint (Huang, et al. 2008; Zhang, et al. 2010; Zhang, et al. 2010), Iris recognition (Bastys, et al. 2009; Chou, et al. 2010; Daugman, 2004; Hollingsworth, et al. 2011) and face recognition (Cament, et al. 2014; Klare, et al. 2012). These common modalities have been widely integrated in numerous systems and devices, which make them useful, but vulnerable regarding potential attacks. Within this context, the biometric community is very active to provide robust solutions in order to prevent identity spoofing. For example, some common spoofing concerns: fake fingerprints for which numerous publications have been dedicated for this purpose (Baldisserra, et al. 2006; Biggio, et al., 2012; Espinoza, et al. 2011; Hadid, et al. 2015). One can also cite other attacked modalities such as: iris recognition, 2D/3D facial recognition, palm-print (including vein biometrics using near infrared). Even though, many interesting and promising advanced solutions have been proposed to overcome this issue, it comes interesting to explore another type of biometrics.

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1. Introduction

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Recently, emerging biometric category is explored. Called Hidden Biometrics, the purpose is the fact that identification and verification processes are performed by extracting features from any part of human body; which is not directly accessible nor visible by naked eye (Nait-Ali, 2011a, 2011b, Aloui, 2012;). Within this context, Hidden Biometrics may require some specific devices or equipment’s that are commonly employed in the clinical and the medical field. For example, Hidden Biometrics can include bio-signals, such as EEG (Electroencephalogram), (Barra, et al. 2016, Kerbaj, et al. 2016), ECG (Electrocardiogram) (Belgacem, et al. 2015), X-ray imaging, (Kabbara, et al. 2013, Kabbara, et al. 2015), and MRI etc.

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In this paper we are exploring human brain images acquired from MRI. The purpose is to extract a unique brainprint from each 3D brain volumetric image to be used either for verification or identification. These images contain folds information, including a set of cortical and subcortical structures. Compared to some common biometric modalities, the proposed approach has a major advantage, which makes attacks/spoofing a difficult task to consider “No one can modify the features of his own brain”. Based on some studies, it comes interesting to deliver some answers to the following questions: Are cortical folding patterns unique to each individual?

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Are brains really asymmetric?

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Is the shape of individual brain structures heritable?

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Are cortical folds and brain shape stable?

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Actually, it is known that human brain consists of white matter, gray matter and cerebrospinal fluid. The cerebral cortex is the outer layer of neural tissue of the cerebrum of the human brain. It is separated into the left and the right hemispheres by the longitudinal fissure. The cerebral cortex is folded, giving a much greater surface area in the confined volume of the skull. A fold or ridge in the cortex is called a gyrus (plural gyri) and a groove or fissure is called a sulcus (plural sulci). In the human brain, more than two-thirds of the cerebral cortex is buried in the

Different studies on the anatomical variability of the brain related to aging and cerebral diseases have shown a change in the brain gray matter, but have confirmed its stability and steadiness in adulthood (Fraley, 2002; Loftus & Loftus, 1980). In addition, this variability affects mainly morphometry and the brain’s shape like volume, and the weight of cortical structures but it does not affect the sulco-gyral patterns. Therefore, the main question that arises within the context of our work is as follows: is it possible to use brain folds as biometric traits for verification/identification routines? Few research attempts have been considered to the feasibility of using brain images for security biometrics. Objectively, one can distinguish two types of approaches; namely, those based on texture analysis and those based on shape and structure analysis. Regarding the first approach, Aloui et al. (Aloui, et al. 2011, Aloui, 2012), used a single slice from a MR volumetric image. In particular, they extracted textural features, from 2D images containing cortical folds, using 1D Log-Gabor transform. Using a similar algorithm as the one employed for iris recognition (Daugman, 1985), a binary template called Braincode was generated. Afterwards, Hamming distance was used for template

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Regarding the second approach, shape information extracted from a set of cortical and subcortical structures has been considered. Aloui et al. (Aloui, et al. 2011, Aloui, et al. 2012), used a single slice from a MR volumetric image. In particular, brain shape descriptors are extracted from of an ellipse that circumscribes the brain. Afterwards, features vector describing the brain shape is generated. For the matching phase, a RangeNormalized Euclidean Distance (RND) has been used for similarity measurement. In another work, Chen et al. (Chen, et al. 2014) implemented brain segmentation algorithm in order to extract gray matter from an input brain image. Then, an alignment based matching algorithm was developed for brain matching. Takao et al. (Takao, et al. 2015) performed brain recognition using voxel-based morphometric approach for image normalization. Principal Component Analysis (PCA) was used for features extraction. For the matching phase, Euclidean distance between image pairs projected into the subspace is calculated.

Curvilinear slices extraction

Planar projection

Image restoration

Gabor features extraction

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Different studies show that, performances of shape-based methods are highly sensitive to segmentation quality and also to the Region of Interest (ROI) analysis techniques. Furthermore, it appears that considering shape representation has the disadvantage of being very sensitive to the inter-subject variabilities of the shape and location of the brain structures, and also to the changes that may be caused by different scanners and acquisition protocols.

5. Performance evaluation: different classifiers have been applied in order to reinforce the hypothesis of uniqueness characteristics biometric modality, to be used for person identification and verification.

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matching. The major drawback of this approach is that a single MR slice acquired at a given distance is used for verification. Considering a single MR slice may provide less reliable descriptors.

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To prevent the disadvantages of shape-based methods evoked above, we investigate in this work, a new representation cerebral cortex from which textural features are extracted and used as brainprints. In particular, our approach considers curvilinear slices that allow planar views of the full cortex, including its cortical folds. This new characterization differs from the previous single planar slice that is limited to only a small part of the cortex.

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This paper is organized as follows: In Section II, we present a general scheme of the proposed modality. Brainprint feature extraction and characterization is detailed Section III. In Section IV, preliminary results over MR Datasets are presented, analyzed and objective performance evaluation is reported. Finally, a conclusion of this research is provided in section 4 2. General methodology

Dimensionality reduction

Recognition

Fig. 1. Framework of the proposed approach.

3. Enrolment and cortical brain characterization In this section we explain in detail the main stages of our approach given by the conceptual framework shown in figure 1. 3.1. Extract curvilinear slices from volumetric MR images The input of our algorithm is a T1-weighted MR image of human brain volume. Technically, this algorithm consists of 4 steps, detailed as illustrated by figure 2.

Skull surface extraction Curvilinear slice extraction

1. Brain image acquisition: due to its high resolution and nonrequirement of radiative contrast medium, structural brain MR images are used.

Curvilinear slice mapping

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Our approach requires six processing phases. A conceptual framework of the present work is shown in figure 1. For some phases, technical details will be provided in the next sections.

2. Curvilinear slices extraction: using 3D volumetric brain scans, curvilinear slices are extracted. After being projected as twodimensional images, a planar view of the cortex, including cortical folds is represented as a texture image. 3. Features extraction: this characterization step is achieved using Gabor transform to extract textural features from projected curvilinear slices. 4. Dimensionality reduction: PCA is used in order to keep exclusively the most relevant characteristics of the brainprints.

Image restoration

Fig. 2. Framework of curvilinear slices extraction. 3 . 1 . 1 . S ku l l s u r f a c e e xt r a c t i o n The purpose of this step is to extract a trianglar mesh surface of inner skull from the brain MRI volume data. We use BET (Brain Extraction Tool) as a fast and fully automated tool for

ACCEPTED MANUSCRIPT skull surface extraction (Smith, 2002). In figure 3, we show the extracted brain and inner skull surfaces.

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3 . 1 . 2 . C u r vi l i n ea r s l i ces ex t r a ct i o n Curvilinear slices extraction from volumetric brain MR image is based on a level-set method. In particular, the triangular mesh of the inner skull surface is transformed into an implicit representation by computing the signed distance function of the surface on a Cartesian grid. In the implicit form, the inner skull + for the signed surface becomes the zero level-set * distance function (Osher & Sethian, 1988; Sigg, et al. 2003). Then, we move this reference surface inward by a speed parameter . The governing equation is: | (

Where (

|

( ) )

): initial level-sets function.

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: is a coefficient that controls the speed and direction of deformation (expands). This constant deformation plays the same role as the pressure force.

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Figure 4 show the upper half of the inner skull sight highlighting many irregularities related to the cortical folds.

Fig. 4. The white curve marks a 2D projection of the inner skull (left). The upper half of the inner skull surface in blue (right).

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Fig. 5. Three computed curvilinear slices for the same brain at three different depths (Up). Related 2D images obtained after planar projection of curvilinear slices (Down). These images obtained from the same brain at different positions spaced by 10 voxels (1cm). 3 . 1 . 3 . C u r vi l i n ea r s l i ces m a p p i n g Once curvilinear slice has been extracted, a hemispherical projection is performed. Technically, cortical part of the brain volume through which pass curvilinear slice is transformed into a 2D slice. Each 2D slice represents a hemispherical shell intersecting the cerebral cortex. Following this transformation the cortex will be transformed into nearly-planar surfaces showing cortical fold structures. This transformation based on mesh parameterization; computes a one-to-one mapping from a 3D triangular surface mesh to a 2D disk (figure 5 down), using Floater Mean Value Coordinates algorithm (Floater, 2003). The ) parameter result of this transformation is a pair of ( coordinates for each vertex of the input mesh.

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Fig. 3. Views of pial surfaces (a) and inner skull surfaces (b and c).

We illustrate the computed curvilinear slices for the same brain at three different depths in figure 5 (Up). Theses curvilinear slices are spaced by 10 voxels (1cm). Each intermediary curvilinear slice is a hemispherical shell obtained at a fixed depth for all studied brains. First, we consider an initial cortical shell, the inner skull surface, as a reference surface as shown in figure 4. Then, this initial surface moves in the direction of the normal inwards of the brain by a speed parameter giving a new curvilinear slice. We extract three curvilinear slices of each brain spaced by 10 voxels (1cm) for all individuals.

Typical projected slices of a T1 weighted MRI volumes are shown in Figure 7. A parameterization of a continuous surface →

is a bijection: ( )

The same definition applies to a discrete mesh where we compute a 2D position ( ) for all the vertices and then interpolates linearly the mapping to the whole piecewise linear geometric mesh. For more details on mesh parameterization, one can refer to various surveys (Floater & Hormann, 2005; Sheffer, et al. 2006). The mapping has to satisfy smoothness assumptions and it requires that each coordinate of has a vanishing Laplacian outside a set of constrained vertices that enforce boundary ( ) is the solution of: conditions. Precisely, {

(

)( ) ()

(

)( ) ()

( )

Where is the boundary of the discrete mesh, which consists in vertices whose face ring is not homeomorphic to a disk. This formulation requires the solution of two sparse linear systems (one for each coordinate of . The boundary condition ( ) for describes a 1D piecewise linear curve in the plane, that is fixed by the user. (

) minimizes:

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3 . 1 . 4 . Im a g es d en o i s i n g a n d r es t o r a t i o n As shown by figure 6, it is clear that the obtained image after mapping is noisy, which is due to information loss in the curvilinear slice mapping step, and due to gray levels reconstruction of the planar images. In these images, a certain amount of the pixels are black. We consider, then, that noise is of type “pepper noise”. For reducing pepper noise, a contraharmonic mean filter can be used.

The contraharmonic mean filtering operation yields a restored image based on the following expression (Marques, 2011): ∑(

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For positive values of , the filter eliminates pepper noise.

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For negative values of

it eliminates salt noise.

(

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Where and define the standard deviations of the Gaussian enveloped along the and direction. As indicated in this equation, a set of Gabor filters can be obtained by appropriate dilations and rotations of the Gabor function ( ). We show in figure 8, a bank of Gabor filters with five scales and eight orientations to extract the texture frequencies and orientation information.

Fig. 8. A set of real impulse responses of Gabor wavelet filters in five scales and eight orientations. Then we used the mean and the standard deviation of the magnitude of the transformed images for each scale and orientation to construct feature vectors, denoted by: ,

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is called the order of the filter :

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Fig. 6. (left) Initial image with “pepper noise” obtained after planar projection of a curvilinear slice. (middel) fitred image using contraharmonic mean filter. (right) Restored image using local histogram equalization.

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( ) is defined as follows (Clausi & Jernigan, 2000; Daugman, 1985) :

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After filtering a “pepper noise” using contraharmonic mean filter, we use local histogram equalization for contrast adjustment (Zhu, et al. 1999).

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We show in figure 7 three two-dimensional images obtained from planar projection of three curvilinear slices obtained from the same brain at different depths paced by 10 voxels (1cm).

Fig. 7. Three two-dimensional images (brainprint) obtained from planar projection of three curvilinear slices for different brains showing the high variability of the cortical folding. 3.2. Gabor features extraction Gabor-wavelet transforms are extensively applied and they represent one of the efficient techniques for texture analysis, image denoising and segmentation (Jain & Healey, 1998; Weldon & Higgins, 1999). We use Gabor-wavelet transforms to detect the local structure of the brainprint image corresponding to spatial frequency (scales), spatial localization and orientation selectivity. Specifically, a two-dimensional Gabor function

PCA is a mathematical tool that is used to obtain a set of uncorrelated principal variables from a number of random variables (Jolliffe, 2014). We apply PCA to each set of extracted Gabor features. Then, we select principal (feature) vectors with the highest eigenvalues. 4. Experimental results To evaluate our new biometric approach based human brainprint recognition, experiments are conducted on OASIS data sets (Open Access Series of Imaging Studies) (Marcus et al., 2007) providing volumetric brain MR images. We have chosen OASIS database because it offers MR brain images for healthy and adult individuals and for each we have 4 different acquisitions. We note that limited available databases propose MR brain images for healthy individuals, including multiple acquisitions. OASIS database is composed of cross-sectional T1weighted structural magnetization-prepared rapid gradient echo (MP-RAGE) images. These images were obtained with the following parameters: TR=9.7ms, TE=4.0ms, slice thickness=1.25mm, slice number=128, ip angle=10°, and inplane resolution=256×256(1mm×1mm). These MR images were corrected for inter-scan head movement and spatially warped into the atlas space of Talairach and Tournoux. We basically deal with MR images corresponding to adult individuals between 18 and 55 years of both sexes and showing no brain pathology (Table 1). We have 220 individuals or classes with 4 volumetric MR images for each unique brain. We extract three curvilinear slices obtained from each volumetric brain image at different positions spaced by 10 voxels (1 cm). Then we have for our tests, 220 classes and 12 brainprint images per class.

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Mean

Total

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Female

<20

19.36

44

17

27

20-30

23.82

98

46

52

30-40

35.28

14

9

5

40-50

46.55

34

12

22

50-60

55.46

30

9

21

220

220

93

Table 3 and 4 and figure 10 show the False Acceptance Rate and the False Rejection Rate with different threshold values. The result illustrates that the proposed biometric recognition system based on brainprint can reach very low FAR and FRR, which makes the brainprint as a promising candidate for verification process. Table 3. The verification rates and rejection rates with different threshold values for linear SVM classifier FAR (%)

FRR (%)

0.25

28.75

3.63

0.65

6.66

0.02

8.25

0.02

8.25

0.00

9.92

0.00

24.46

0.38 0.45 0.45 0.52 0.75

Table 4. The verification rates and rejection rates with different threshold values for Medium Gaussian SVM classifier

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The Gabor wavelet features are extracted using Gabor wavelet transform. Gabor wavelet features are extracted by applying Gabor wavelet kernels with five different scales and four orientations on mask size of 128*128. The energy vector length for each window of 4*4 is twenty; due to the use of Gabor filters in 4 orientations and 5 scales. So, the length of Gabor features vector is 20480. Afterword, we applied PCA to each set of extracted features, obtaining principal (feature) vectors from which we select those associated to the highest eigenvalues. Up to 300 eigenvectors of each set are used for classification purposes.

Threshold

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matching scores corresponding to the imposter and genuine matching scores. The best performance of our approach will be obtained by selecting an appropriate value of the threshold.

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Several classifiers methods such as support vector machines (SVM), k-nearest neighbor classifier (KNN), Bagged Trees classifier and subspace discriminant classifier are applied to classify features. When dealing with identification, results are compared using three performance criteria: Correct Match Rate CMR, False Positive Rate FPR and False Negative Rate FNR. Table 2 summarizes the identification performances achieved by the individual classifiers. Obtained results show that our identification system achieves excellent performances in the testing database. These results indicate that our algorithm is capable to differentiate brain images at a high rate of accuracy. The reader can notice that linear SVM with RBF kernels achieve significantly higher CMR of 99.64%.

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Table 2. Three performance criterias: Correct Match Rate CMR, False Positive Rate FPR and False Negative Rate FNR for each classifier. CMR (%)

FPR (%)

FNR (%)

Linear SVM

99.64

0.037

0.379

Fine KNN

98.78

1.035

0.384

99.44

0.265

0.380

99.54

0.151

0.379

Medium Gaussian SVM

99.61

0.075

0.379

Subspace Discriminant

99.54

0.151

0.379

Bagged Trees

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Classifier

When considering verification mode, each brainprint image in the database is matched with the other brainprint images. Genuine matching indicates that two matching brainprint images are acquired from the same individual, while imposter matching indicates that two matching brainprint images are scanned from different subjects. The distributions of genuine and imposter for linear SVM and medium Gaussian SVM classifiers are shown in figure 9. When matching score is close to zero, both brainprints are considered in this case differents and related to two different subjects. However, when matching score is close to 1, in this case, brainprints are considered related to the same subject. Also, we observed, for each classifier, two peaks in the distribution of

Threshold

FAR (%)

FRR (%)

0.46

19.85

3.56

0.55

5.08

5.98

0.58

2.64

6.36

0.68

0.16

9.01

0.73

0.02

10.75

0.85

0.00

23.10

Figure 11 shows the Receiver Operating Curves ROC plotting false acceptance rate FAR versus false rejection rate FRR for 2 classifiers. This can be considered as interesting performances. Using these curves, one can extract other metric of performances such as the Area Under ROC Curves AUC and Equal Error Rate EER, as given in Table 3. An EER=2.90±0.47 and an AUC=97.08±0.53 are reached when considering verification mode. These results illustrate that the proposed verification system based brainprint can reach very high verification rates at low rejection rates. When we compare our approach to previous approaches based on geometric features of cortical and subcortical structures (eg. volume, area of cortical and subcortical structures), one can note that these features will be affected by brain atrophy. The silcogyral patterns may be more robust to brain changes, assuming that the folding patterns of the brain remain stable. Our approach can reach higher rates than the maximum accuracy of 97.53% in (Aloui, et al. 2011) and 94.46% in (Aloui, et al. 2011; Aloui, et al. 2012). In Chen’s method, a maximum accuracy of 99.46% and an EER=3.88% are reached. Nevertheless, the main shortage of this method developed therein is that it’s based on the segmentation of the brain structures and matching of gray matter intensities. This make, the performances highly sensitive to the applied segmentation algorithms and the ROI analysis techniques. Obviously, the high performances we obtained assume that brain structures are unique for each individual. Since our study

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Table 5. Two performance criterias: Area Under ROC Curve AUC and Equal Error Rate EER for different classifiers with 95% interval of confidence. Classifier

AUC (%)

EER (%)

Linear SVM

97.67±0.61

5.76±0.56

Fine KNN

97.08±0.53

2.93±0.53

Bagged Trees

97.72±0.54

5.73±0.58

Quadratic SVM

97.76±0.59

5.31±0.54

Medium Gaussian SVM

97.27±0.68

5.69±0.55

Subspace Discriminant

97.81±0.46

2.90±0.47

5. Conclusion

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Through this research, we introduced a new approach to extract a robust biometric signature (i.e. brainprint), from MR brain images. As we saw, the brainprint is computed by mapping curvilinear slices (that include the cerebral cortex), into 2D images. This new brain folds representation produces symmetrical views of the cortex and allows low intervariabilities. Feature extraction was achieved through Gabor wavelet transform from projected curvilinear slices. As result, we achieved a correct classification rate of 99.64%, and an EER=2.90±0.47 is obtained in verification. These results make this emerging robust biometric modality a promising solution to overcome some spoofing issues. As a concept, it may be considered especially if high-level security is required. However, it is obvious that nowadays, the acquisition aspect is not suited for fast security applications. Current MRI machines are rather designed for medical applications. The cost is still expensive and the acquisition process is time consuming. For the future, we strongly believe that it comes interesting to design compact and optimized MRI devices, dedicated specifically to security applications.

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considers basically data, acquired from individuals followed over a period of up to 3 years, the assessment cannot predict the evolution of the accuracy for the whole life. Extending the database is challenging.

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Besides the application we considered in this work, we believe that our approach may be applied for other purposes such as: (1) brain asymmetry and inter variability analysis (2) age and gender estimation, including in forensics applications (3) genetic influences on brain morphology.

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The authors gratefully acknowledge the support of University of Tunis El Manar and University of Paris-Est. We acknowledge the use of OASIS database provided by the Washington University Alzheimer’s Disease Research Center, Dr. Randy Buckner at the Howard Hughes Medical Institute (HHMI) at Harvard University, the Neuroinformatics Research Group (NRG) at Washington University School of Medicine, and the Biomedical Informatics Research Network (BIRN). References Aloui, K. (2012, December 17). Caractérisation du cerveau humain : application à la biométrie (phdthesis). Université Paris-Est.

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Fig. 9. Distributions of genuine and imposter matching scores; (left) Distribution of matching scores related to Linear SVM classifier. (Right) Distribution

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Fig. 10. The False Acceptance Rate FAR and the False Rejection Rate FRR with different threshold values for two classifiers: Quadratic SVM and

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Fig. 11. The ROC curves related to Linear SVM and Medium Gaussian SVM classifiers.

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