CURRENT iris recognition systems claim to perform

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1 1 Improving Iris Recognition Performance using Segmentation, Quality Enhancement, Match Score Fusion and Indexing Mayank Vatsa, Richa Singh, and Afzel Noore Abstract This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and speed of iris recognition. A curve evolution approach is proposed to effectively segment a non-ideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of iris image. A SVM based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good quality regions to create a single high quality iris image. Two distinct features are extracted from the high quality iris image. The global textural feature is extracted using 1D log polar Gabor transform and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, while an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms are validated and compared with other algorithms using CASIA Version 3, ICE 2005, and UBIRIS iris databases. Index Terms Iris Recognition, Mumford-Shah Curve Evolution, Quality Enhancement, Information Fusion, Support Vector Machine, Iris Indexing. I. INTRODUCTION CURRENT iris recognition systems claim to perform with very high accuracy. However, these iris images are captured in a controlled environment to ensure high quality. Daugman proposed an iris recognition system representing iris as a mathematical function [1]-[4]. Wildes [5], Boles [6], and several other researchers proposed different recognition algorithms [7]-[32]. With a sophisticated iris capture setup, users are required to look into the camera from a fixed distance and the image is captured. Iris images captured in an uncontrolled environment produce non-ideal iris images with varying image quality. If the eyes are not opened properly, certain regions of the iris cannot be captured due to occlusion which further affects the process of segmentation and consequently the recognition performance. Images may also suffer from motion blur, camera diffusion, presence of eyelids and eyelashes, head rotation, gaze direction, camera angle, reflections, contrast, luminosity, and problems due to contraction and dilation. Fig. 1 from the UBIRIS database [26], [27] shows images with some of the above mentioned problems. These artifacts in iris M. Vatsa, R. Singh, A. Noore are with Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA {mayankv, richas, noore}@csee.wvu.edu. images increase the false rejection rate (FRR) thus decreasing the performance of recognition system. Experimental results from the Iris Challenge Evaluation (ICE) 2005 and ICE 2006 [30], [31] also show that most of the recognition algorithms have high FRR. Table I compares existing iris recognition algorithms with respect to image quality, segmentation, enhancement, feature extraction, and matching techniques. A detailed literature survey of iris recognition algorithms can be found in [28]. This research effort focuses on reducing the false rejection by accurate iris detection, quality enhancement, fusion of textural and topological iris features, and iris indexing. For iris detection, some researchers assume that iris is circular or elliptical. In non-ideal images such as off-angle iris images, motion blur and noisy images, this assumption is not valid because the iris appears to be non-circular and non-elliptical. In this research, we propose a two-level hierarchical iris segmentation algorithm to accurately and efficiently detect iris boundaries from non-ideal iris images. The first level of iris segmentation algorithm uses intensity thresholding to detect approximate elliptical boundary and the second level applies Mumford Shah functional to obtain the accurate iris boundary. We next describe a novel Support Vector Machine (SVM) based iris quality enhancement algorithm [29]. The SVM quality enhancement algorithm identifies good quality regions from different globally enhanced iris images and combines them to generate a single high quality feature-rich iris image. Textural and topological features [17], [18] are then extracted from the quality enhanced image for matching. Most of the iris recognition algorithms extract features that provide only global information or local information of iris patterns. In this paper, the feature extraction algorithm extracts both global texture features and local topological features. The texture features are extracted using 1D log polar Gabor transform which is invariant to rotation and translation, and the topological features are extracted using Euler number which is invariant under translation, rotation, scaling, and polar transformation. The state-of-art iris recognition algorithms have a very low false acceptance rate but reducing the number of false rejection is still a major challenge. In multibiometric literature [33], [34], [35], [36], it has been suggested that fusion of information extracted from different classifiers provides better performance compared to single classifiers. In this paper, we propose using 2ν-SVM to develop a fusion algorithm that combines the match scores obtained by matching texture and topological features for improved performance. Further,

2 2 TABLE I COMPARISON OF EXISTING IRIS RECOGNITION ALGORITHMS. Research Quality Iris Image Feature extraction Additional paper assessment segmentation enhancement and matching comments Daugman Frequency Integro- Neural network + 2D Gabor First iris [1]-[4] approach differential operator - transform + Hamming distance recognition algorithm Wildes [5] Using high Image intensity gradient - Laplacian of Gaussian filters + - contrast edges and Hough transform normalized correlation Boles and - Edge - Wavelet transform zero crossing + Does not perform for Boashash [6] detection dissimilarity function non-ideal iris images Ma et al. Frequency based Gray-level information Background Multichannel spatial filter + fisher- Does not work [12] SVM classification and canny edge detection subtraction linear discriminant classification with occluded images Ma et al. - Gray-level information Background 1D iris signal operated on Local features are [13] and canny edge detection subtraction Dyadic wavelet + similarity function used for recognition Avila and - Intensity based - Gabor filter and multiscale zero-crossing + Does not unwrap Reillo [16] detection Euclidean and Hamming distance the iris image - Intensity based - 1D log polar Gabor and Euler number + Rule based decision Vatsa et al. detection Hamming distance and L1 distance strategy is used [18] to improve accuracy Monro et al. - Heuristic gray-level Background 1D DCT + Hamming distance Fast feature extraction [24] edge feature subtraction and matching Poursaberi and - Morphological operators Wiener Daubechies 2 wavelet + Hamming - Araabi [25] and thresholds 2D filter distance and harmonic mean - Active contours and - Gaze deviation correction, Daugman [32] generalized coordinates - Iris Code second rank in ICE 2006 and low time complexity recognition can be divided into verification and identification. The performance of both verification and identification suffer due to non-ideal acquisition issues. However, identification is more difficult compared to verification because of the problem of high penetration rate and false accept rate. To improve the identification performance, we propose an iris indexing algorithm. In the proposed indexing algorithm, the Euler code is first used to filter possible matches. This subset is further processed using the texture features and 2ν-SVM fusion for accurate identification. Section II presents the proposed non-ideal iris segmentation algorithm and Section III describes the novel quality enhancement algorithm. Section IV briefly explains the extraction of global features using 1D log polar Gabor transform and the extraction of local features using Euler number. Section V describes the intelligent match score fusion algorithm and Section VI presents the indexing algorithm to reduce the average identification time. The details of iris databases and existing algorithms used for validation of the proposed algorithm is presented in Section VII. Section VIII and IX summarize the verification and identification performance of the proposed algorithms with existing recognition and fusion algorithms. II. NON-IDEAL IRIS SEGMENTATION ALGORITHM Processing non-ideal iris images is a challenging task because the iris and pupil are non-circular and the shape varies depending on how the image is captured. The first step in iris segmentation is the detection of pupil and iris boundaries from the input eye image and unwrapping the extracted iris into a rectangular form. Researchers have proposed different algorithms for iris detection. Daugman [1] applied integrodifferential operator to detect the boundaries of iris and pupil. The segmented iris is then converted into rectangular form by applying polar transformation. Wildes [5] used the first derivative of image intensity to find the location of edges corresponding to the iris boundaries. This system explicitly models the upper and lower eyelids with parabolic arcs whereas Daugman excludes the upper and the lower portions of the image. Boles and Boashash [6] localized and normalized the iris by using edge detection and other computer vision algorithms. Ma et al. [12], [13] used Hough transform to detect the iris and pupil boundaries. Normally, the pupil has a dark color and the iris has a light color with varying pigmentation. In certain nonideal conditions, the iris can be dark and the pupil can appear illuminated. For example, because of the specular reflections from the cornea or co-axial illumination directly into the eye, light is reflected into the retina and back through the pupil which makes the pupil appear bright. Also, the boundary of non-ideal iris image is irregular and cannot be considered exactly circular or elliptical. For such non-ideal and irregular iris images, researchers have recently proposed segmentation algorithms which combine conventional intensity techniques with active contours for pupil and iris boundary detection [32], [37]-[39]. These algorithms use intensity based techniques for center and pupil boundary detection. The pupil boundary is used to initialize the active contour which evolves to find the outer boundary of iris. This method of evolution from pupil to the outer iris boundary is computationally expensive. We therefore propose a 2-stage iris segmentation algorithm in which we first estimate the inner and outer boundaries of iris using an elliptical model. In the second stage, we apply the modified Mumford Shah functional [40] in a narrow band over the estimated boundaries to compute the exact inner and outer boundaries of the iris. To identify the approximate boundary of pupil in non-ideal eye-images, an elliptical region with major axis a = 1, minor axis b = 1, and center (x, y) is selected as the center of eye and the intensity values are computed for a fixed number of points on the circumference. The parameters of the ellipse (a, b, x, y, θ) are iteratively varied with a step size of two

3 3 (a) (b) (c) to the maximum intensity change give the outer boundary of the iris and the center of this ellipse gives the center of the iris. This method thus provides approximate iris and pupil boundaries, corresponding centers, and major and minor axis. Some researchers assume the center of pupil to be the center of iris and compute the outer boundary. While this helps to simplify the modeling, in reality, this assumption is not valid for non-ideal iris. Computing outer boundary using the proposed algorithm provides accurate segmentation even when the pupil and iris are not concentric. Using these approximate inner and outer boundaries, we now perform the curve evolution with modified Mumford-Shah functional [40], [41] for iris segmentation. In the proposed curve evolution method for iris segmentation, the model begins with the following energy functional: Energy(c) = α Ω β +λ C φ dc + c in(c) out(c) I(x, y) c 1 2 dxdy I(x, y) c 2 2 dxdy (1) (d) where C is the evolution curve such that C = {(x, y) : ψ(x, y) = 0}, c is the curve parameter, φ is the weighting function or stopping term, Ω represents the image domain, I(x, y) is the original iris image, c 1 and c 2 are the average value of pixels inside and outside C respectively, and α, β, and λ are positive constants such that α < β λ. Parameterizing Equation 1 and deducing the associated Euler- Lagrange equation leads to the following active contour model, (e) Fig. 1. Iris images representing the challenges of iris recognition (a) Iris texture occluded by eyelids and eyelashes, (b) Iris images of an individual with different gaze direction, (c) Iris images of an individual showing the effects of contraction and dilation, (d) Iris images of same individual at different instances: First image is of good quality and second image has motion blurriness and limited information present, (e) Images of an individual showing the effect of natural luminosity factor [26]. pixels to increase the size of ellipse and every time a fixed number of points are randomly chosen on the circumference (in the experiments, it is set to be 40 points) to calculate the total intensity value. This process is repeated to find the boundary with maximum variation in intensity and the center of the pupil. The approximate outer boundary of the iris is also detected in a similar manner. The parameters for outer boundary a 1, b 1, x 1, y 1, and θ 1 are varied by setting the initial parameters to the pupil boundary parameters. A fixed number of points (in the experiments, it is set to be 120 points) are chosen on the circumference and the sum of the intensity values is computed. Values corresponding ψ t = αφ( ν+ɛ k ) ψ + φ ψ+βδ(i c 1 ) 2 +λδ ψ(i c 2 ) 2 (2) where ν is the advection term, ɛ k is the curvature based smoothing term, is the gradient operator and δ = 0.5/(π(x )). The stopping term φ is defined as, φ = ( I ) 2 (3) The active contour ψ is initialized to the approximate pupil boundary and the exact pupil boundary is computed by evolving the contour in a narrow band [42] of ±5 pixels. Similarly, for computing the exact outer iris boundary, the approximate iris boundary is used as the initial contour ψ and the curve is evolved in a narrow band [42] of ±10 pixels. Using the stopping term, φ, the curve evolution stops at the exact outer iris boundary. Since we are using the approximate iris boundaries as the initial ψ, the complexity of curve evolution is reduced and is suitable for real-time applications. Fig. 2 shows the pupil and iris boundaries extracted using the proposed non-ideal iris segmentation algorithm. In non-ideal cases, eyelids and eyelashes may be present as noise and decrease the recognition performance. Using the technique described in [1], eyelids are isolated by fitting lines to the upper and lower eyelids. A mask based on the detected

4 4 Fig. 2. Iris detection using the proposed non-ideal iris segmentation algorithm. eyelids and eyelashes is then used to extract the iris without noise. Image processing of iris is computationally intensive as the area of interest is of donut shape and grabbing the pixels in this region requires repeated rectangular to polar conversion. To simplify this, the detected iris is unwrapped into a rectangular region by converting into polar coordinates. Let I(x, y) be the segmented iris image and I(r, θ) be the polar representation obtained using Equations 4 and 5. r = (x x c ) 2 + (y y c ) 2 0 r r max (4) ( ) y θ = tan 1 yc x x c r and θ are defined with respect to the center coordinates, (x c, y c ). The center coordinates obtained during approximate elliptical iris boundary fitting are used as the center point for cartesian to polar transformation. The transformed polar iris image is further used for enhancement, feature extraction, and matching. III. GENERATION OF SINGLE HIGH QUALITY IRIS IMAGE USING ν-support VECTOR MACHINE For iris image enhancement, researchers consecutively apply selected enhancement algorithms such as deblurring, denoising, entropy correction, and background subtraction, and use the final enhanced image for further processing. Huang et al. [43] used super-resolution and Markov network for iris image quality enhancement but their method does not perform well with unregistered iris images. Ma et al. [12] proposed background subtraction based iris enhancement that filters the high frequency noise. Poursaberi and Araabi [25] proposed the use of low pass Wiener 2D filter for iris image enhancement. However, these filtering techniques are not effective in mitigating the effects of blur, out of focus, and entropy based irregularities. Another challenge with existing enhancement techniques is that they enhance the low quality regions present in the image but are likely to deteriorate the good quality regions and alter the features of the iris image. A non-ideal iris image containing multiple irregularities may require the application of specific algorithms to local regions that need enhancement. However, identifying and isolating these local regions in an iris image can be tedious, time consuming, and (5) not pragmatic. In this paper, we address the problem by concurrently applying a set of selected enhancement algorithms globally to the original iris image [29]. Thus each resulting image contains enhanced local regions. These enhanced local regions are identified from each of the transformed images using support vector machine [44] based learning algorithm and then synergistically combined to generate a single high quality iris image. Let I be the original iris image. For every iris image in the training database, a set of transformed images is generated by applying standard enhancement algorithms for noise removal, defocus, motion blur removal, histogram equalization, entropy equalization, homomorphic filtering, and background subtraction. The set of enhancement functions is expressed as, I 1 I 2 I 3 = f noise (I) = f blur (I) = f focus (I) I 4 = f histogram (I) (6) I 5 I 6 I 7 = f entropy (I) = f filter (I) = f background (I) where f noise is the algorithm for noise removal, f blur is the algorithm for blur removal, f focus is the algorithm for adjusting the focus of the image, f histogram is the histogram equalization function, f entropy is the entropy filter, f filter is the homomorphic filter for contrast enhancement, and f background is the background subtraction process. I 1, I 2, I 3, I 4, I 5, I 6, and I 7 are the resulting globally enhanced images obtained when the above enhancement operations are applied to the original iris image I. Applying several global enhancement algorithms does not uniformly enhance all the regions of the iris image. A learning algorithm is proposed to train and classify the pixel quality from corresponding locations of the globally enhanced iris images. This knowledge is used by the algorithm to identify the good quality regions from each of the transformed and original iris images, and combined to form a single high quality iris image. The learning algorithm uses ν-svm [45] which is expressed as,

5 5 f(x) ( m ) = sgn α i y i k(x, x i ) + b i=1 m α i y i = 0 (7) i=1 m α i ν i=1 where ν ɛ [0, 1], x i is the input to ν-svm, y i is the corresponding label, m is the number of tuples, α i is the dual variable, and k is the RBF kernel. Further, a fast implementation of ν-svm [46] is used to decrease the time complexity. Training involves classifying the local regions of the input and global enhanced iris image as good or bad. Any quality assessment algorithm can be used for this task. However, in this research we have used the redundant discrete wavelet transformation based quality assessment algorithm described in [47]. To minimize the possibility of errors due to the quality assessment algorithm, we also verify the labels manually and correct them in case of errors. The labeled training data is then used to train the ν-svm. The training algorithm is described as follows: The training iris images are decomposed to l levels using Discrete Wavelet Transform. The 3l detail subbands of each image contain the edge features and thus these bands are used for training. The subbands are divided into windows of size 3 3 and the activity level of each window is computed. The ν-svm is trained using labeled iris images to determine the quality of every wavelet coefficient. The activity levels computed in the previous step are used as input to the ν-svm. The output of training algorithm is ν-svm with a separating hyperplane. The trained ν-svm labels the coefficient G or 1 if it is good and B or 0 if the coefficient is bad. Next the trained ν-svm is used to classify the pixels from input image and to generate a new feature-rich high quality iris image. The classification algorithm is described as follows: The original iris image and the corresponding globally enhanced iris images generated using Equation 6 are decomposed to l DWT levels. The ν-svm classifier is then used to classify the coefficients of the input bands as good or bad. A decision matrix, Decision, is generated to store the quality of each coefficient in terms of G and B. At any position (i, j), if the SVM output O(i, j) is positive then that coefficient is labeled as G, otherwise it is labeled as B. { G if O(i, j) 0 Decision(i, j) = (8) B if O(i, j) < 0 The above operation is performed on all eight images including the original iris image and a decision matrix corresponding to every image is generated. For each of the eight decision matrices, the average of all coefficients with label G is computed and the coefficients having label B are discarded. In this manner, one fused approximation band and 3l fused detail subbands are generated. Individual processing of every coefficient ensures that the irregularities present locally in the image are removed. Further, the selection of good quality coefficients and removal of all bad coefficients addresses multiple irregularities present in one region. Inverse DWT is applied on the fused approximation and detail subbands to generate a single feature-rich high quality iris image. In this manner, the quality enhancement algorithm enhances the quality of the input iris image and a feature-rich image is obtained for feature extraction and matching. Fig. 3 shows an example of the original iris image, different globally enhanced images, and the combined image generated using the proposed iris image quality enhancement algorithm. IV. IRIS TEXTURE AND TOPOLOGICAL FEATURE EXTRACTION AND MATCHING ALGORITHMS Researchers have proposed several feature extraction algorithms to extract unique and invariant features from iris image. These algorithms use either texture or appearance based features. The first algorithm was proposed by Daugman [1] which used 2D Gabor for feature extraction. Wildes [5] applied isotropic band-pass decomposition derived from the application of Laplacian of Gaussian filters to the iris image. It was followed by several different research papers such as, Ma et al. [12], [13] in which multichannel even-symmetric Gabor wavelet and the multichannel spatial filters were used to extract textural information from iris patterns. The usefulness of the iris features depends on the properties of basis function and the feature encoding process. In this paper, the iris recognition algorithm uses both global and local properties of an iris image. 1D log polar Gabor transform [48] based texture feature [17], [18] provides the global properties which are invariant to scaling, shift, rotation, illumination, and contrast. Topological features [17], [18] extracted using Euler number [49] provide local information of iris patterns and are invariant to rotation, translation, and scaling of the image. The following two subsections briefly describe the textural and topological feature extraction algorithm. 1) Texture Feature Extraction using 1D Log Polar Gabor Wavelet: The texture feature extraction algorithm [17], [18] uses 1D log polar Gabor transform [48]. Like Gabor transform [50], log polar Gabor transform is also based on polar coordinates but unlike the frequency dependence on a linear graduation, the dependency is realized by a logarithmic frequency scale [50], [51]. Therefore, the functional form of 1D log polar Gabor transform is given by: G r0θ 0 (θ) = exp [ 2π 2 σ 2[ {ln( r r 0 f )} 2 τ 2 +{2ln(f 0sin(θ θ 0))} 2]] (9) where (r, θ) are the polar coordinates, r 0 and θ 0 are the initial values, f is the center frequency of the filter and f 0 is the parameter that controls the bandwidth of the filter. σ and τ are defined as follows:

6 6 Fig. 3. Original iris image, seven globally enhanced images and the SVM enhanced iris image. σ = 1 ln2 πln(r 0 )sin(π/θ 0 ) 2 τ = 2ln(r 0)sin(π/θ 0 ) ln2 ln2 2 (10) (11) Gabor transform is symmetric with respect to the principal axis. During encoding, Gabor function over-represents the low frequency components and under-represents the high frequency components [48], [50], [51]. In contrast, log polar Gabor transform shows maximum translation from center of gravity in the direction of lower frequency and flattening of the high frequency part. The most important feature of this filter is invariance to rotation and scaling. Also, log polar Gabor functions have extended tails and encode natural images more efficiently than Gabor functions. To generate an iris template from 1D log polar Gabor transform, the 2D unwrapped iris pattern is decomposed into a number of 1D signals where each row corresponds to a circular ring on the iris region. For encoding, angular direction is used rather than radial direction because maximum independence occurs along this direction. 1D signals are convolved with 1D log polar Gabor transform in frequency domain. The values of the convolved iris image are complex in nature. Using these real and imaginary values, the phase information is extracted and encoded in a binary pattern. If the convolved iris image is I g (r, θ), then the phase feature P(r, θ) is computed using Equation 12. ( ) Im P(r, θ) = tan 1 Ig (r, θ) Re I g (r, θ) (12) [1, 1] if 0 0 < P(r, θ) 90 0 [0, 1] if 90 I p (r, θ) = 0 < P(r, θ) [0, 0] if < P(r, θ) (13) [1, 0] if < P(r, θ) Phase features are quantized using the phase quantization process represented in Equation 13 where I p (r, θ) is the resulting binary iris template of 4096 bits. Fig. 4 shows the iris template generated using this algorithm. Fig. 4. Binary iris templates generated using 1D log polar Gabor transform. (a) and (b) are iris templates of the same individual at two different instances. To verify a person s identity, the query iris template is matched with the stored templates. For matching the textural iris templates, we use Hamming distance [1]. The match score, MS texture, for any two texture-based masked iris templates, A i and B i, is computed using Hamming distance measure given by Equation 14. MS texture = 1 N N A i B i (14) i=1 where N is the number of bits represented by each template and is the XOR operator. For handling rotation, the templates are shifted left and right bitwise and the match scores are calculated for every successive shift [1]. The smallest value is used as the final match score, MS texture. The bitwise

7 7 shifting in the horizontal direction corresponds to rotation of the original iris region at an angle defined by the angular resolution. This also takes into account the misalignments in the normalized iris pattern which are caused due to the rotational differences during imaging. 2) Topological Feature Extraction using Euler Number: Convolution with 1D log polar Gabor transform extracts the global textural characteristics of the iris image. To further improve the performance, local features represented by the topology of iris image are extracted using Euler numbers [18], [49]. For a binary image, Euler number is defined as the difference between the number of connected components and the number of holes. Euler numbers are invariant to rotation, translation, scaling, and polar transformation of the image [18]. Each pixel of the unwrapped iris can be represented as an 8-bit binary vector {b 7, b 6, b 5, b 4, b 3, b 2, b 1, b 0 }. These bits form eight planes with binary values. As shown in Fig. 5, four planes formed from the four most significant bits (M SB) represent the structural information of iris, and the remaining four planes represent the brightness information [49]. The brightness information is random in nature and is not useful for comparing the structural topology of two iris images. TABLE II EULER CODE OF AN INDIVIDUAL AT THREE DIFFERENT INSTANCES. Euler code MSB1 MSB2 MSB3 MSB4 Image Image Image the Euler code has large variance, it increases the false reject rate. Mahalanobis distance ensures that the features having high variance do not contribute to the distance. Applying Mahalanobis distance measure for comparison thus avoids the increase in false reject rate. The topology based match score is computed as, MS topology = D(x, y) log 10 max(d) (16) where, max(d) is the maximum possible value of Mahalanobis distance between two Euler codes. The match score of Euler codes is the normalized Mahalanobis distance between two Euler codes. Fig. 5. image. Binary images corresponding to eight bit planes of the masked polar For comparing two iris images using Euler code, a common mask is generated for both the iris images to be matched. The common mask is generated by performing a bitwise-or operation of the individual masks of the two iris images and is then applied to both the polar iris images. For each of the two iris images with common mask, a 4-tuple Euler code is generated which represents the Euler number of the image corresponding to the four MSB planes. Table II shows the Euler Codes of a person at three different instances. We use Mahalanobis distance to match the two Euler codes. Mahalanobis distance between two vectors is defined as, D(x, y) = (x y) t S 1 (x y) (15) where, x and y are the two Euler codes to be matched and S is the positive definite covariance matrix of x and y. If V. FUSION OF TEXTURE AND TOPOLOGICAL MATCHING SCORES Iris recognition algorithms have succeeded in achieving a low false acceptance rate but reducing the rejection rate remains a major challenge. To make iris recognition algorithms more practical and adaptable to diverse applications, the false rejection rate needs to be reduced significantly. In [33], [35], [36], [52], it has been suggested that the fusion of match scores from two or more classifiers provides better performance compared to a single classifier. In general, match score fusion is performed using sum rule, product rule, or other statistical rules. Recently in [35], a kernel based match score fusion algorithm has been proposed to fuse the match scores of fingerprint and signature. In this section, we propose using 2ν-SVM [53] to fuse the information obtained by matching the textural and the topological features of iris image that are described in Section IV. The proposed fusion algorithm reduces the false rejection rate while maintaining a low false acceptance rate. Let the training set be Z = (x i, y i ) where i = 1,..., N. N is the number of multimodal scores used for training and y i (1, 1), where 1 represents the genuine class and - 1 represents the impostor class. SVM is trained using these labeled training data. The mapping function ϕ( ) is used to map the training data into a higher dimensional feature space such that Z ϕ(z). The optimal hyperplane which separates the higher dimensional feature space into two different classes in the higher dimensional feature space can be obtained using 2ν-SVM [53]. We have {x i, y i } as the set of N multimodal scores with x i ɛ R d. Here, x i is the i th score that belongs to the binary class y i. The objective of training 2ν-SVM is to find the hyperplane that separates two classes with the widest margins, i.e.,

8 8 subject to, to minimize, wϕ(x) + b = 0 (17) y i (w ϕ(x) + b) (ρ ψ i ), ξ i 0 (18) 1 2 w 2 i C i (νρ ξ i ) (19) where ρ is the position of margin and ν is the error parameter. ϕ(x) is the mapping function used to map the data space to the feature space and provide generalization for the decision function that may not be a linear function of the training data. C i (νρ ξ i ) is the cost of errors, w is the normal vector, b is the bias, and ξ i is the slack variable for classification errors. ν can be calculated using ν + and ν, which are the error parameters for training the positive and negative classes respectively. ν = 2ν + ν ν + + ν, 0 < ν + < 1 and 0 < ν < 1 (20) Error penalty C i is calculated as, { C+, if y C = i = +1 C, if y i = 1 where, C + = C = (21) ( [n ν )] 1 + (22) ν ( [n 1 + ν )] 1 (23) ν + and n + and n are the number of training points for the positive and negative classes respectively. 2ν-SVM training can be formulated as, max (αi) 1 α i α j y i y j K(x i, x j ) (24) 2 where, i,j 0 α i C i i α iy i = 0 i α i ν i, j ɛ 1,..., N and kernel function is (25) K (x i, x j ) = ϕ(x i )ϕ(x j ) (26) Kernel function K(x i, x j ) is chosen as the radial basis function. The 2ν-SVM is initialized and optimized using iterative decomposition training [53], which leads to reduced complexity. In the testing phase, fused score f t of a multimodal test pattern x t is defined as, f t = f (x t ) = wϕ(x t ) + b (27) The solution of this equation is the signed distance of x t from the separating hyperplane given by 2ν-SVM. Finally, an accept or reject decision is made on the test pattern x t using a threshold X: { accept, if output of SV M X Result(x t ) = reject, if otherwise (28) Fig. 6 presents the steps involved in the proposed 2ν-SVM learning algorithm which fuses the texture and topological match scores for improved classification. VI. IRIS IDENTIFICATION USING EULER CODE INDEXING Iris recognition can be used for verification (1:1 matching) as well as identification (1:N matching). Apart from the irregularities due to non-ideal acquisition, iris identification suffers from high system penetration and false accept cases. For identification, a probe iris image is matched with all the gallery images and the best match is rank #1 match. Due to the poor quality and non-ideal acquisition, rank 1 match may not be the correct match and lead to false acceptance. The computational time for performing iris identification on large databases is another challenge [31]. For example, identifying an individual from a database of 50 million users requires an average of 25 million comparisons. On such databases, applying distance based iris code matching or the proposed SVM fusion will take significant amount of time. Parallel processing and improved hardware can reduce the computational time at the expense of operational cost. Other techniques which can be used to speedup the identification process are classification and indexing. Yu et al. [19] proposed a coarse iris classification technique using fractals which classifies iris images into four categories. The classification technique improves the performance in terms of computational time but compromises the identification accuracy. Mukherjee [54] proposed an iris indexing algorithm in which block based statistics is used for iris indexing. Single pixel difference histogram used in the indexing algorithm yields good performance on a subset of CASIA version 3.0 database. However, the indexing algorithm is not evaluated for non-ideal poor quality iris images. In this paper, we propose feature based iris indexing algorithm for reducing the computational time required for iris identification without compromising the identification accuracy. The proposed indexing algorithm is a two step process where the Euler code is first used to generate a small subset of possible matches. The 2ν-SVM match score fusion algorithm is then used to find the best matches from the list of possible matches. The proposed indexing algorithm is divided into two parts: (1) feature extraction and database enrollment in which features are extracted from the gallery images and indexed using Euler code and (2) probe image identification in which features from the probe image are extracted and matched. A. Feature Extraction and Database Enrollment Compared to the feature extraction and matching algorithm described in Section IV, we use a slightly different strategy for feature extraction. For verification, we use common

9 9 Fig. 6. Steps involved in the proposed 2ν-SVM match score fusion algorithm. masks from gallery and probe images to hide the eyelids and eyelashes. However, in indexing, we do not follow the same method because generating common mask for every set of probe and gallery images will increase the computational cost. Using the iris center coordinates, the X and Y axis are drawn and the iris is divided into four regions. Researchers have shown that regions A and B in Fig. 7 contain minimum occlusion due to eyelids and eyelashes, and hence are the most useful for iris recognition [4], [9], [22]. Therefore for indexing we use regions A and B to extract features. The extracted features are stored in the database and Euler code is used as the indexing parameter. A B A B S = s(i) (30) i=1 where, s = 4 is the intermediate score vector that provides the number of matched Euler values. We extend this scheme for iris identification by matching the indexing parameter of the probe image with the gallery images. Let n be the total number of gallery images and S n represent the indexing scores corresponding to the n comparisons. The indexing scores, S n, are sorted in descending order and the top M match scores are selected as possible matches. For every probe image, the Euler code based indexing scheme yields a small subset of top M matches from the gallery where M << n (for instance, M = 20 and n = 2000). To further improve the identification accuracy, we apply the proposed 2ν-SVM match score fusion. We then use the algorithms described in Sections IV and V to match the textural and topological features of the probe image with top M matched images from the gallery and compute the fused match score for each of the M gallery images. Finally, these M fused match scores are again sorted and a new ranking is obtained to determine the identity. A B A B VII. DATABASES AND ALGORITHMS USED FOR PERFORMANCE EVALUATION AND COMPARISON In this section, we describe the iris databases and algorithms used for evaluating the performance of the proposed algorithms. Fig. 7. Iris image divided into four parts. Regions A and B are used in the proposed iris indexing algorithm. B. Probe Image Identification Similar to the database enrollment process, features are extracted from the probe iris image and Euler code is used to find the possible matches. For matching two iris indexing parameters (Euler codes), E 1 (i) and E 2 (i) (i = 1, 2, 3, 4), we apply a thresholding scheme. Indexing parameters are said to be matched if E 1 (i) E 2 (i) T where T is the geometric tolerance constant. Indexing score S is computed using Equations 29 and 30. { 1 if E1 (i) E s(i) = 2 (i) T (29) 0 otherwise A. Databases used for Validation To evaluate the performance of the proposed algorithms, we selected three iris databases namely ICE 2005 [30], [31], CA- SIA Version 3 [55], and UBIRIS [26], [27]. These databases are chosen for validation because the iris images embody irregularities captured with different instruments and device characteristics under varying conditions. The databases also contain iris images from different ethnicity and facilitates a comprehensive performance evaluation of the proposed algorithms. ICE 2005 database [30], [31] used in recent Iris Challenge Evaluation contains iris images from 244 iris classes. The total number of images present in the database is 2,953. CASIA Version 3 database [55] contains 22,051 iris images pertaining to more than 1,600 classes. The images

10 10 have been captured using different imaging setup. The quality of images present in the database also varies from high quality images with extremely clear iris texture details to images with nonlinear deformation due to variations in visible illumination. Unlike the CASIA Version 1 where artificially manipulated images were present, the CASIA Version 3 contains original unmasked images. UBIRIS database [26], [27] is composed of 1,877 images from 241 classes captured in two different sessions. The images in the first session are of good quality whereas the images captured in the second session have irregularities in reflection, contrast, natural luminosity and focus. B. Existing Algorithms used for Validation To evaluate the effect of the proposed quality enhancement algorithm on different feature extraction and matching techniques, we implemented Daugman s integro-differential operator and neural network architecture based 2D Gabor transform described in [1]-[4]. We also used the Masek s iris recognition algorithm obtained from [11]. Further, the performance of the proposed 2ν-SVM fusion algorithm is compared with Sum rule [33], [34], Min/Max rule [33], [34], and kernel based fusion rule [35]. VIII. PERFORMANCE EVALUATION AND VALIDATION FOR IRIS VERIFICATION In this section, we evaluate the performance of the proposed segmentation, enhancement, feature extraction, and fusion algorithms for iris verification. The performance of the proposed algorithms is validated using the databases and algorithms described in Section VII. For validation, we divided the databases into three parts: training dataset, gallery dataset, and probe dataset. Training dataset consist of manually labeled one good quality and one bad quality image per class. This dataset is used to train the ν-svm for quality enhancement and 2ν- SVM for fusion. After training, the good quality image in the training dataset is used as the gallery dataset and the remaining images are used as the probe dataset. The bad quality image of the training dataset is not used for either gallery or probe dataset. For iris segmentation, we performed extensive experiments to compute a common set of curve evolution parameters that can be applied to detect exact boundaries of iris and pupil from all the databases. The values of different parameters for segmentation with narrow band curve evolution are α = 0.2, β = 0.4, λ = 0.4, advection term ν = 0.72, and curvature term ɛ k = These values provide accurate segmentation results for all three databases. Fig. 8 shows sample results demonstrating the effectiveness of the proposed iris segmentation algorithm on all the databases with different characteristics. The inner yellow curve represents the pupil boundary and the outer red curve represents the iris boundary. Fig. 8 also shows that the proposed segmentation algorithm is not affected by different types of specular reflections present in the pupil region. Using the proposed iris segmentation and quality enhancement algorithms, we then evaluated the verification performance with the textural and topological features. The match Fig. 8. CASIA Version 3 Database ICE 2005 Database UBIRIS Database Results of the proposed iris segmentation algorithm. scores obtained from texture and topological features were fused using 2ν-SVM to further evaluate the proposed fusion algorithm. Figs show the ROC plots for iris recognition using the textural feature extraction, topological feature extraction, and 2ν-SVM match score fusion algorithms. Fig. 9 shows the ROC plot for the ICE 2005 database [30] and Fig. 10 shows the results for the CASIA Version 3 database [55]. The ROC plots show that the proposed 2ν-SVM match score fusion performs the best followed by the textural and topological features based verification. The false rejection rate of individual features is high but the fusion algorithm reduces it significantly and provides the FRR of 0.74% at % false accept rate (FAR) on the ICE 2005 database and 0.38% on the CASIA Version 3 database. The results on the ICE 2005 database also show that the verification performance of the proposed fusion algorithm is comparable to the three best algorithms in the Iris Challenge Evaluation 2005 [31]. The same set of experiments is performed using the UBIRIS database [26]. The images in this database contain irregularities due to motion blur, off angle, gaze direction, diffusion, and other real world problems that enable us to evaluate the robustness of the proposed algorithms on non-ideal iris images. Fig. 11 shows the ROC plot obtained using the UBIRIS database. In this experiment, the best performance of 7.35% FRR at % FAR is achieved using the 2ν-SVM match score fusion algorithm. The high rate of false rejection is due to cases where the iris is partially visible. Examples of such cases are shown in Fig. 12. The experimental results on all three databases are summarized in Table III. In this table, it can be seen that the proposed fusion algorithm significantly reduces the false rejection rate.

11 11 Fig. 12. Sample iris images from the UBIRIS database [26] on which the proposed algorithms fail to perform. TABLE IV EFFECT OF THE PROPOSED IRIS IMAGE QUALITY ENHANCEMENT ALGORITHM AND PERFORMANCE COMPARISON OF IRIS RECOGNITION ALGORITHMS. False rejection rate (%) at % false accept rate with different enhancement algorithms Daugman s implementation [1], [4] Masek s algorithm [11] Proposed 2ν-SVM fusion algorithm Database None Wiener Background Proposed None Wiener Background Proposed None Wiener Background Proposed filter subtraction SVM filter subtraction SVM filter subtraction SVM CASIA ICE UBIRIS Topological Texture SVM Match Score Topological Texture SVM Match Score False Rejection Rate(%) False Rejection Rate(%) False Accept Rate(%) False Accept Rate(%) Fig. 9. ROC plot showing the performance of the proposed algorithms on the ICE 2005 database [30]. Fig. 10. ROC plot showing the performance of the proposed algorithms on the CASIA Version 3 database [55]. However, the rejection rate cannot be reduced if a closed eye image or an eye image with limited information is present for matching. We next evaluated the effectiveness of the proposed iris image quality enhancement algorithm and compared with existing enhancement algorithms namely Wiener filtering [25] and background subtraction [12]. Table IV shows the results for the proposed and existing verification algorithms when the original iris image is used and when the quality enhanced images are used. For the ICE 2005 database, this table shows that without enhancement, the proposed 2ν-SVM fusion algorithm gives 1.99% FRR at % FAR. The performance improves by 1.25% when the proposed iris image quality enhancement algorithm is used. We also found that the proposed SVM image quality enhancement algorithm outperforms existing enhancement algorithms by at least 0.89%. Similar results are obtained for other two iris image databases. SVM iris image quality enhancement algorithm also improves the performance of existing iris recognition algorithms. SVM enhancement algorithm performs better because SVM locally removes the irregularities such as blur and noise, and enhances the intensity of the iris image whereas Wiener filter only removes the noise and background subtraction algorithm only highlights the features by improving the image intensity. We further compared the performance of the proposed 2ν- SVM fusion algorithm with Daugman s iris detection and recognition algorithms [1]-[4] and Masek s implementation of iris recognition [11]. The results in Table IV show that the proposed 2ν-SVM fusion yields better performance compared to the Daugman s and Masek s implementation because the

12 12 False Rejection Rate(%) Topological Texture SVM Match Score False Accept Rate(%) Fig. 11. ROC plot showing the performance of the proposed algorithms on the UBIRIS iris database [26]. TABLE V COMPARISON OF EXISTING FUSION ALGORITHMS WITH THE PROPOSED 2ν-SVM FUSION ALGORITHM ON THE ICE 2005 DATABASE. Fusion Algorithm FRR at % FAR (%) Min/Max rule [33], [34] 1.91 Sum rule [33], [34] 1.57 Kernel based fusion [35] 1.48 Proposed 2ν-SVM fusion 0.74 TABLE VI AVERAGE TIME TAKEN FOR THE STEPS INVOLVED IN THE PROPOSED IRIS RECOGNITION ALGORITHM. Algorithm Time (ms) Iris segmentation 908 ν-svm quality enhancement 347 Feature extraction and matching 211 2ν-SVM fusion 94 Average execution time 1560 TABLE III PERFORMANCE COMPARISON OF THE PROPOSED ALGORITHMS ON THREE IRIS DATABASES. FRR (%) at % FAR Algorithm CASIA 3 ICE 2005 UBIRIS database database database Topological features Texture features ν-SVM match score fusion ν-SVM fusion algorithm uses multiple cues extracted from the iris image and intelligently fuses the matching scores such that the false rejection is reduced without increasing the false acceptance rate. Higher performance of the proposed algorithm is also due to the accurate iris segmentation obtained using the modified Mumford Shah functional. Further, the performance of the proposed 2ν-SVM fusion algorithm is compared with Sum rule [33], [34], Min/Max rule [33], [34], and kernel based fusion algorithms [35]. The performance of the proposed and existing fusion algorithms is evaluated on the ICE 2005 database by fusing the match scores obtained from the texture and topological features. Table V shows that the proposed 2ν-SVM fusion algorithm performs the best with 0.74% FRR at % FAR which is 0.74% better than kernel based fusion algorithm [35] and 0.83% better than Sum rule [33]. These results thus show that the proposed fusion algorithm effectively fuses the textural and topological features of iris image, enhances the recognition performance, and reduces the false rejection rate considerably. The average time for matching two iris images is 1.56 seconds on Pentium- IV 3.2 GHz processor with 1 GB RAM under C programming environment. Table VI shows the breakdown of computational complexity in terms of the average execution time for iris segmentation, enhancement, feature extraction and matching, and 2ν-SVM fusion. IX. PERFORMANCE EVALUATION AND VALIDATION FOR IRIS IDENTIFICATION In this section, we present the performance of the proposed indexing algorithm for iris identification. Similar to verification, we use segmentation, enhancement, feature extraction, and fusion algorithms described in Sections II - V. To validate the performance of the proposed iris indexing algorithm, we combine the three iris databases and generate a nonhomogeneous database with 2085 classes and 26,881 images. The experimental setup (training dataset, gallery dataset, probe dataset, and segmentation parameters) is similar to the setup used for iris verification. Using the training dataset, we found the value of geometric tolerance constant T = 20. Fig. 13 shows the Cumulative Matching Characteristics (CMC) plots for the proposed indexing algorithm with and without the 2ν-SVM match score fusion. The plots show that rank #1 identification accuracy of 92.39% is achieved when indexing algorithm is used without match score fusion. The accuracy improves to 97.21% with the use of 2ν-SVM match score fusion. Incorporating textural features and match score fusion thus reduces the false accept rate and provides an improvement of around 5% in rank #1 identification accuracy. We also observed that on the nonhomogeneous database, 100% accuracy could not be achieved because the database contains occluded images with very limited information similar to those shown in Fig. 12. We next compared the identification performance of Daugman s iriscode algorithm and the proposed 2ν-SVM match score fusion without indexing. Daugman s algorithm is used as a baseline for comparison. Daugman s algorithm yields the identification accuracy of 95.89% and the average time required for identifying an individual is 5.58 seconds. On the other hand, the identification accuracy of the proposed 2ν-SVM match score fusion algorithm without indexing is 97.21%. However, the average time for identifying an individual is seconds which is considerably higher than Daugman s algorithm. To reduce the significant time taken for identification, the proposed indexing algorithm described in Section VI is used. Indexing is achieved by using the Euler

13 13 Identification Accuracy (%) Indexing without Match Score Fusion (Case 1) Indexing with Match Score Fusion (Case 3) Top M Matches (Rank) Fig. 13. CMC plot showing the identification accuracies obtained by the proposed indexing algorithm. code which is computed from the local topological features of the iris image. The indexing algorithm identifies a small subset of the most likely candidates that will yield a match. Specifically, we analyze three scenarios. Case 1 determines a match based on the local topological feature match score. Case 2 is an extension that uses the subset of images identified with the local features. However, the matching is based on the global texture feature match score. Case 3 is a further extension that fuses the match scores obtained from the local and global features to perform identification. The identification performance is determined by experimentally computing the accuracy and the time taken for identification. The results are summarized in Table VII for all three cases when indexing is used with the proposed recognition algorithm. In all three scenarios, the proposed algorithm considerably decreases the identification time, thereby making it suitable for real-time applications and the use with large databases. In Case 1, since only the local Euler feature is used for indexing, the identification time is the fastest (0.043 seconds); however the accuracy is lower compared to Daugman s algorithm. The accuracy improved when both the global and local features are used sequentially. Further, as shown in Table VII, Case 3 yields the best performance in terms of accuracy (97.21%) with an average identification time of less than 2 seconds. X. CONCLUSION In this paper we address the challenge of improving the performance of iris verification and identification. The paper presents an accurate non-ideal iris segmentation using the modified Mumford-Shah functional. Depending on the type of abnormalities likely to be encountered during image capture, a set of global image enhancement algorithms is concurrently applied to the iris image. While this enhances the low quality regions, it also adds undesirable artifacts in the original high quality regions of the iris image. Enhancing only selected regions of the image is extremely difficult and not pragmatic. This paper describes a novel learning algorithm that selects enhanced regions from each globally enhanced image and synergistically combines to form a single composite high quality iris image. Furthermore, we extract global texture features and local topological features from the iris image. The corresponding match scores are fused using the proposed 2ν-SVM match score fusion algorithm to further improve the performance. Iris recognition algorithms require significant amount of time to perform identification. 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15 15 [48] D.J. Field, Relations between the statistics of natural images and the response properties of cortical cells, Journal of the Optical Society of America, vol. 4, pp , [49] A. Bishnu, B.B. Bhattacharya, M.K. Kundu, C.A. Murthy, and T. Acharya, Euler vector for search and retrieval of graytone images, IEEE Transactions on Systems, Man and Cybernetics-B, vol. 35, no. 4, pp , [50] C. Palm and T.M. Lehmann, Classification of color textures by Gabor filtering, Machine Graphics and Vision, vol. 11, no. 2/3, pp , [51] D. J. Field, What is the goal of sensory coding? Neural Computation, vol. 6, pp , [52] Y. Wang, T. Tan, and A.K. Jain, Combining face and iris biometrics for identity verification, Proceedings of the Fourth International Conference on Audio and Video Based Biometric Person Authentication, pp , [53] H.G. Chew, C.C. Lim, and R.E. Bogner, An implementation of training dual-nu support vector machines, In L.Qi, K.L.Teo and X.Yang (Eds), Optimization and Control with Applications, Kluwer, [54] R. Mukherjee, Indexing techniques for fingerprint and iris databases, Master s Thesis, West Virginia University, [55] Afzel Noore received his Ph.D. in Electrical Engineering from West Virginia University. He worked as a digital design engineer at Philips India. From 1996 to 2003, Dr. Noore served as the Associate Dean for Academic Affairs and Special Assistant to the Dean in the College of Engineering and Mineral Resources at West Virginia University. He is a Professor in the Lane Department of Computer Science and Electrical Engineering. His research interests include computational intelligence, biometrics, software reliability modeling, machine learning, hardware description languages, and quantum computing. His research has been funded by NASA, NSF, Westinghouse, GE, Electric Power Research Institute, the US Department of Energy, and the US Department of Justice. Dr. Noore has over 85 publications in refereed journals, book chapters, and conferences. He has received four best paper awards. Dr. Noore is a member of the IEEE and serves in the editorial boards of Recent Patents on Engineering and the Open Nanoscience Journal. He is a member of Phi Kappa Phi, Sigma Xi, Eta Kappa Nu, and Tau Beta Pi honor societies. Mayank Vatsa is a graduate research assistant in the Lane Department of Computer Science and Electrical Engineering at West Virginia University. He is currently pursuing his Doctoral degree in Computer Science. He had been actively involved in the development of a multimodal biometric system which includes face, fingerprint, signature, and iris recognition at Indian Institute of Technology Kanpur, India from July 2002 to July His current areas of interest are pattern recognition, image processing, uncertainty principles, biometric authentication, watermarking, and information fusion. Mayank has more than 65 publications in refereed journals, book chapters, and conferences. He has received four best paper awards. He is a member of the IEEE, Computer Society, and ACM. He is also a member of Phi Kappa Phi, Tau Beta Pi, Sigma Xi, Upsilon Pi Epsilon, and Eta Kappa Nu honor societies. Richa Singh is a graduate research assistant in the Lane Department of Computer Science and Electrical Engineering at West Virginia University. She is currently pursuing her Doctoral degree in Computer Science. She had been actively involved in the development of a multimodal biometric system which includes face, fingerprint, signature, and iris recognition at Indian Institute of Technology Kanpur, India from July 2002 to July Her current areas of interest are pattern recognition, image processing, machine learning, granular computing, biometric authentication, and data fusion. Richa has more than 65 publications in refereed journals, book chapters, and conferences, and has received four best paper awards. She is a member of the IEEE, Computer Society, and ACM. She is also a member of Phi Kappa Phi, Tau Beta Pi, Upsilon Pi Epsilon, and Eta Kappa Nu honor societies.