Chapter-2 LITERATURE REVIEW ON IRIS RECOGNITION SYTSEM

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1 Chapter-2 LITERATURE REVIEW ON IRIS RECOGNITION SYTSEM This chapter presents a literature review of iris recognition system. The chapter is divided mainly into the six sections. Overview of prominent iris recognition methods is taken in section 2.1. Detailed reviews of the existing methods for iris segmentation stage and iris normalisation stages are described in section 2.2 and section 2.3 respectively. Literature survey on iris analysis along with existing methods for iris feature extraction and encoding stage is detailed in section 2.4. Section 2.5 describes the existing methods for iris template matching. Review of quality performance measures in iris recognition methods is discussed in section 2.6. Finally, the inferences drawn from this literature survey and relevance of the proposed work have been defined in section Review of Iris Recognition Methods Iris recognition technique is one of the biometric verification and identification techniques which also include fingerprint, facial, retinal and many other biological features. They all present novel solutions for human being recognition, authentication and security applications [3,7]. The iris has been in use as biometric from few decades. However, the idea of automating iris recognition is more recent. In 1987, Flom and Safir obtained a patent for an unimplemented conceptual design of an automated iris biometrics system [24] with the concept that no two irises are alike. The pioneering work in the early history of iris biometrics is that of Daugman. Daugman s 1994 patent [13] and early publications became a standard reference model. Integro-differential operators are used to detect the center and diameter of the Literature review on Iris recognition system 14

2 iris. The image is converted from Cartesian coordinates to polar coordinates and the rectangular representation of the region of the interest is generated. Feature extraction algorithm uses the 2D Gabor wavelets to generate the iris codes which are then matched using Hamming distance (Daugman, 2004). The algorithm gives the accuracy of more than 99.99%. Also the time required for the iris identification is less than 1 Sec. The Hough transform considers a set of edge points and finds the circle that best fits the most edge points. In matching two irises, Daugman s approach involves computation of the normalized Hamming distance between iris codes, whereas Wildes applies a Laplacian of Gaussian filter at multiple scales to produce a template. Matching is achieved via an application of normalized correlation and Fisher s linear discriminant as a similarity measure. Wildes briefly describes [20] the results of two experimental evaluations of the approach, involving images from several hundreds of irises. This paper demonstrates that multiple distinct technical approaches exist for each of the main modules of an iris biometrics system. Wildes [21] approach involves computing a binary edge map followed by a Hough transform to detect circles. Tan et.al. [25] proposes several innovations, and then provide a comparison of different methods and algorithms of iris recognition. The iris is localized in several steps which first find a good approximation for the pupil center and radius, and then apply the Canny operator and the Hough transform to locate the iris boundaries more precisely. The iris image is converted to dimensionless polar coordinates, similarly to Daugman, and then is processed using a variant of the Gabor filter. The dimension of the signature is reduced via an application of the Fisher linear discrimant. A careful statistical performance evaluation is provided for the authors work, and for most of the well-known algorithms mentioned above [26]. M. Vatsa et. al. [28-31] presented a novel iris verification algorithm which uses textural and topological features of the iris image. The proposed 1D log Gabor wavelet is used to extract the textural information and Euler numbers are used to extract the topological information from the iris image. They used Hamming distance Literature review on Iris recognition system 15

3 algorithm and proposed difference matching algorithm to match the textural and topological information. Based on this algorithm, matching strategy is presented to reduce the false rejection while false acceptance is unaffected. Li. Ma [32-34] used vertical, horizontal projections and Hough transform for localization followed by dyadic wavelet for feature vector generation. Boles and Boashash [37] have given an algorithm that locates the pupil centre using an edge detection method, records grey level values on virtual concentric circles, and then constructs the zero-crossing representation on these virtual circles based on a one-dimensional dyadic wavelet transform. Corresponding virtual circles in different images are determined by rescaling the images to have a common iris diameter. The authors create two dissimilarity functions for the purposes of matching, one using every point of the representation and the other using only the zero crossing points. The algorithm has been tested successfully on a small database of iris images, with and without noise. In another approach, by Li Ma et. al.[35] multichannel and Even symmetry Gabor filters are used to capture local texture information of the iris, which are used to construct fixed length feature vector. Nearest feature line method is used for iris matching. The result obtained were 0.01% for false acceptance rate and 2.17% for false rejection rate. Zhu et. al. [41] used Gabor filters and 2D wavelet transform for feature extraction. For identification, weighted Euclidean distance classification has been used. This method is invariant to translation and rotation and tolerant to illumination. The classification rate on using Gabor is 98.3% and accuracy with wavelet is 82.51%. Several interesting ideas are presented by Lim, et al., in [44]. Following a standard iris localization and conversion to polar coordinates relative to the center of the pupil, the authors propose alternative approaches to both feature extraction and matching. For feature extraction they compare the use of the Gabor Transform and the Haar Wavelet Transform, and their results indicate that the Haar Transform is somewhat better. Using the Haar transform the iris patterns can be stored using only 87 bits, which compares well to the 2,048 required by Daugman s algorithm. The matching process uses an LVQ competitive learning neural network, which is optimized by a Literature review on Iris recognition system 16

4 careful selection of initial weight vectors. Also, a new multi-dimensional algorithm for winner selection is proposed. Experimental results are given in [45] based on a database of images of irises from 200 people. The pre-processing stage is standard. Edge detection is performed using the Canny method, and each iris image is then transformed to standardized polar coordinates relative to the center of the pupil as proposed by Du, et al.[46]. The feature extraction stage is quite different from those mentioned previously, and is simple to implement. The authors use a gray scale invariant called Local Texture Patterns (LTP) that compares the intensity of a single pixel to the average intensity over a small surrounding rectangle. The LTP is averaged in a specific way to produce the elements of a rotation invariant vector. Thus the method performs a loss projection from 2D to 1D. This vector is then normalized so that its elements sum to one. The matching algorithm uses the Du measure, which is the product of two measures, one based on the tangent of the angle between two vectors p and q, and the other based on the relative entropy of q with respect to p, otherwise known as the Kullback- Liebler distance. Another paper involving Du [47], in the context of hyperspectral imaging, provides evidence that the Du measure is more sensitive than either of the other two measures. Even though iris recognition has shown to be extremely accurate for user identification, there are still some issues remaining for practical use of this biometric [43]. For example, the fact that the human iris is about 1 cm in diameter makes it very difficult to be imaged at high resolution without sophisticated camera systems. Traditional systems require user cooperation and interaction to capture the iris images. By observing the position of their iris on the camera system while being captured, users adjust their eye positions in order to localize the iris contour accurately [44]. This step is crucial in iris recognition since iris features cannot be used for recognition unless the iris region is localized and segmented correctly. Many iris localization techniques exist and have been developed. Some of the classical methods for iris localization are Daugman s integro-differential operator [18], and Wildes Hough transform [20]. In order to compensate the variations in the pupil size and in the image capturing distances, the segmented iris region is mapped into a fixed length and Literature review on Iris recognition system 17

5 dimensionless polar coordinate system [13]. In terms of feature extraction, iris recognition approaches can be divided into three major categories: phase-based methods, zero-crossing methods, and texture analysis based methods. Finally, the comparison between iris templates is made, and a metric is measured. If this value is higher than a threshold, the system outputs a non-match, meaning that each signature belongs to different irises. Otherwise, the system outputs a match, meaning that both templates were extracted from the same iris. In next sections, a literature review on each stage of iris recognition system can be presented in next sections. 2.2 Review of Existing Methods for Iris Segmentation Stage In this section, review is taken of most existing methods of iris localization that were developed for iris recognition system also we will investigate and discuss the performance and accuracy for each proposed method. All early research in iris segmentation assumed that the iris had a circular boundary. However, often the pupillary and limbic boundaries are not perfectly circular. Recently, Daugman has studied alternative segmentation techniques to better model the iris boundaries [18]. Even when the inner and outer boundaries of the iris are found, some of the iris still may be occluded by eyelids or eyelashes. Upon isolating the iris region, the next step is to describe the features of the iris in a way that facilitates comparison of irises. Many researches had proposed well-known techniques in detecting pupil and iris boundaries such as Hough Transform, Daugman's integro-differential and active contour models. These methods are most common approaches that are used in iris localization stage. More details about these methods and algorithms is described below Hough transform : The Hough transform is a famous technique to locate any regular curves by a given geometric shape, or shapes in a given image that can be defined in a parametric form such as lines, parabolas, ellipses, polynomials and circles. The Hough transform is connected by gaps in the edges. Circular Hough transform was used to localize irises Literature review on Iris recognition system 18

6 by Wildes et al. [20], Kong and Zhang [48], and Ma et al. [35]. Wildes proposed this method; it generates the points of an edge map by computing the first derivatives of the image intensity values and thresholding the results value. Hough transform techniques have some drawbacks. First, threshold values are required for edge detection, and removing in critical edge points may lead to fail detection circles or arcs. Second, the Hough transform also needs a large memory and a lot of computations that need special hardware requirements and make it inappropriate solution especially for real time applications Integro-differential operator : Integro-differential operator was proposed by Daugman to localize the iris and pupil boundaries [18]. This operator assumes that both pupil and limbus are full circular contours and performs as a circular edge detector. Also, Integro-differential operator is used to detect the upper and lower eyelids by modifying the contour search from circular to an arc. The integro-differential can describes as a variation of the Hough transform because it depends on the first derivatives of the image, and it executes a search to find geometric parameters. But, it excludes thresholding problems of the Hough transform, since it works with raw derivative information. However, the integrodifferential algorithm has critical disadvantages which make it work only on local scale and it fails when there is noise in the eye image, such as light reflections, occlusions and eyelashes Active contour model : Wildes [20] used edge detectors and Hough transforms to segment the eye image. The problem in this method was that the noises of pupil and eyelashes cannot be eliminated. The other problem for this method that cost a high computational process because it performs searching process among all of the potential candidates to detect eyelid, the method proposed by Wildes, tried to enhance the Daugman s model using some constrains to locate the true edge points of the eyes. Ma et. al. [34], presented a new method based on Hough transform method. He enhanced the Hough method by adding some filters to the original image and he Literature review on Iris recognition system 19

7 added new method to find the edge points. In his method, the noise of eyelashes was cancelled, but the pupil parts problem still exists in the segmented area. Huang et al. [53], tried to solve the main disadvantages of Daugman s integrodifferential method in two ways. First, he reduced the computation time, second he tried to solve the problem of pupil center detection when it is located outside the image. But he didn t solve the problem of the noises caused by eyelashes. Kong and Zhang [48], to detect iris boundary by using new methods for mapping the image from Radial coordinate to Cartesian coordinate. In this method the pupil parts is not cancelled but at least they solved the eyelashes problem. Huang et al. [54], proposed iris localization method using texture segmentation. Abhyankar et.al. [55-56] propose using active shape models for finding the elliptical iris boundaries of off-angle images. A recent paper by Daugman [18] presents alternative methods of segmentation based on active contours, a way to transform an off-angle iris image into a more frontal view, and a description of new score normalization scheme to use when computing Hamming distance that would account for the total amount of unmasked data available in the comparison. In another effort to improve performance, there has been an army of literature presenting different modifications on how to extract texture information from the segmented iris region. 2.3 Review of Iris Normalization Methods The first difficulty in iris recognition lies in the fact that not all images of an iris are the same size. The distance from the camera affects the size of the iris in the image. Also, changes in illumination can cause the iris to dilate or contract. This problem was addressed by mapping the extracted iris region into a normalized coordinate system. To accomplish normalization is performed. Every location on the iris image is defined by two coordinates, (i) an angle between 0 and 360 degrees, and (ii) a radial coordinate that ranges between 0 and 1 regardless of the overall size of the image. This normalization assumes that the iris compresses or stretches linearly in the radial direction when the pupil dilates or contracts, respectively. Wyatt [57] explains that this assumption is a good approximation, but it does not perfectly match the actual Literature review on Iris recognition system 20

8 deformation of an iris. The normalized iris image can be displayed as a rectangular image, with the radial coordinate on the vertical axis, and the angular coordinate on the horizontal axis. The left side of the normalized image marks 0 degrees on the iris image, and the right side marks 360 degrees. The division between 0 and 360 degrees is somewhat arbitrary, because a simple tilt of the head can affect the angular coordinate. The process is inherently dimensionless in the angular direction. In the radial direction, the texture is assumed to change linearly, which known as the rubber sheet model. The rubber sheet model linearly maps the iris texture in the radial direction from pupil border to limbus border between interval [0-1] and creates a dimensionless transformation in the radial direction as well. Although the normalization method compensates variations due to scale, translation and pupil dilation, it is not inherently invariant to the rotation of iris. Rotation of an iris in the Cartesian coordinates is equivalent to a shift in the polar coordinates. In order to compensate the rotation of iris textures, a best of n test of agreement technique is proposed by Daugman in the matching process. In his method, iris templates were shifted and compared into n different directions for compensate the rotational effects. Wildes [20], developed an image registration technique, which geometrically warps a newly acquired image, I (x, y) a into alignment with a selected database image I (x, y) d [ ]. They used a mapping function (u(x, y),v(x, y)) to transform the original coordinates, the image intensity values of the new image made to be close to those of corresponding points in the reference image. Non-linear normalization method is proposed by Liu [58], considers a nonlinear behavior of iris patterns due to changes of pupil size. In order to unwrap an iris region properly, a non-linear model and a linear normalization model are combined. The non-linear method, which is first applied to an iris image, is based on the few assumptions. The pupil and iris boundaries are concentric circles. The margin of the pupil does not rotate significantly during pupil size changes. Further, it is assumed that, pupil shape does not change and remain circular when pupil size changes. Literature review on Iris recognition system 21

9 2.4 Review of Existing Methods at Iris Feature Encoding Stage Daugman [13] uses convolution with 2-dimensional Gabor filters to extract the texture from the normalized iris image. In his system, the filters are multiplied by the raw image pixel data and integrated over their domain of support to generate coefficients which describe, extract, and encode image texture information. After the texture in the image is analyzed and represented, it is matched against the stored representation of other irises. If iris recognition were to be implemented on a large scale, the comparison between two images would have to be very fast. Thus, Daugman chose to quantize each filter s phase response into a pair of bits in the texture representation. Each complex coefficient was transformed into a two-bit code: the first bit was equal to 1 if the real part of the coefficient was positive, and the second bit was equal to 1 if the imaginary part of the coefficient was positive. Thus after analyzing the texture of the image using the Gabor filters, the information from the iris image was summarized in a 256byte (2048 bit) binary code. A detailed comparison of seven different filter types is given by Thornton et al. [59-62]. They consider the Haar wavelet, Daubechies wavelet, order three, Coiflet wavelet, order one, Symlet wavelet, order two, Biorthogonal wavelet, orders two and two, circular symmetric filters, and Gabor wavelets. They applied a single bandpass filter of each type and determined that the Gabor wavelet gave the best equal error rate. They then tune the parameters of the Gabor filter. Wavelets process have the essential advantage over traditional Fourier transform in that the frequency data is localized, allowing features which occur at the same position and resolution to be matched up. Many of wavelet filters, also called a bank of wavelets, are applied to the 2D iris region, one for each resolution with each wavelet a scaled version of some basis function. The output of applying the wavelets process is then encoded in order to provide a compact and discriminating representation of the iris pattern. In order to perform wavelets process, a feature vector is used to describe an image or some measures obtained from wavelet coefficients. Literature review on Iris recognition system 22

10 In this step an image is transformed using a Discrete Wavelet Transform (DWT), then several mathematical operations are applied in order to observe some features presented in the image. The transformation process is important to reveal some features that are not clear enough or has difficulties in detecting the original domain. Therefore Wavelets can be used to decompose the data in the iris region into components that appears at different resolutions. Gabor filters are used to provide optimum conjoint representation of a signal in space and spatial frequency. A Gabor filter is constructed by modulating a sine/cosine wave with a Gaussian which able to provide the optimum conjoint localization in both space and frequency, since a sine wave is perfectly localized in frequency, but not localized in space. Modulation of the sine with a Gaussian provides localization in space, though with loss of localization in frequency. Decomposition of a signal is accomplished using a quadrature pair of Gabor filters, with a real part specified by a cosine modulated by a Gaussian, and an imaginary part specified by a sine modulated by a Gaussian. The real and imaginary filters are also known as the even symmetric and odd symmetric components respectively. The centre frequency of the filter is specified by the frequency of the sine/cosine wave, and the bandwidth of the filter is specified by the width of the Gaussian. Daugman designed a 2D version of Gabor filters [16] in order to use for encoding iris pattern data. Daugman was suggested to demodulate the output of the Gabor filters in order to compress the data. This can be performed by quantizing the information into four levels, for each possible quadrant in the complex plane. These four levels of phase information are represented by using two bits of data, which each pixel in the normalized iris pattern corresponds to these two bits of data in the iris template. A total of 2,048 bits are calculated for iris template, and an equal number of masking bits are generated in order to mask out the corrupted regions within the iris image. This creates a compact 256-byte template, which allows for efficient storage and comparison of irises. Using Gabor filter have essential disadvantage that the even symmetric filter used there will be a DC component whenever the bandwidth is larger than one octave Literature review on Iris recognition system 23

11 [72]. However, zero DC components can be obtained for any bandwidth by using a Gabor filter which is calculated as Gaussian on a logarithmic scale; this is known as the Log-Gabor filter. Boles and Boashash [37] designed the (1D) wavelets for encoding iris pattern data. The authors create two dissimilarity functions for the purposes of matching, one using every point of the representation and the other using only the zero crossing points. The algorithm has been tested successfully on a small database of iris images, with and without noise. Li. Ma. et al. [26] was used the wavelet transform to extract features from the iris region. For most recent studies both the Gabor transform and the Haar wavelet are considered as the mother wavelet methods. From multi-dimensionally filtering, a feature vector with 87 dimensions is computed. Since each dimension has a real value ranging from -1.0 to +1.0, the feature vector is sign quantized so that any positive value is represented by 1 and negative value as 0. Lim et al. compared the use of Gabor transform and Haar wavelet transform and showed that the recognition rate of Haar wavelet transform is slightly better than Gabor transform by 0.9%. Wildes [23], had designed a system to encode feature via decomposes the iris region by applying Laplacian of Gaussian filters to the iris region image. The filtered image is represented as a Laplacian pyramid which allowed compressing the data, so that only significant data remains. A Laplacian pyramid is constructed with four different resolution levels in order to generate a compact iris template. Dorairaj et.al.[65] proposed an efficient iris feature extraction approach called Independent Component Analysis (ICA) was used to establish features for iris region of interest which was statistically independent. ICA is a statistical technique for decomposing a complex dataset into independent sub-parts. It has been applied successfully for many different problems such as feature extraction. Sun et. al. [86] proposed statistical feature extraction along like concentric circles in the area of iris is introduced. In this approach after edge detection, we can get inner and outer edges of iris as well as pupils area. By using the center of pupil and inner edge, we can draw various sizes of lines like concentric circles along which include statistical features that are computed. The statistical features considered in the Literature review on Iris recognition system 24

12 iris feature extraction process are (a) Mean (b) Median (c) Mode (d) Variance and (e) Standard deviation of the circles 2.5 Review of Existing Methods for Iris Template Matching Stage After localized process for the region of an acquired image that corresponds to the iris for extracting its feature, the final task is performed to check if this encoded pattern matches a previously stored iris pattern. For accomplishing such task, many algorithms need to perform for matching two bit patterns the important features to do matching; the best iris pattern matching method is used. Daugman uses a metric called the normalized Hamming distance, which measures the fraction of bits for which two iris codes disagree. A low normalized Hamming distance implies strong similarity of the iris codes. If parts of the irises are occluded, the normalized Hamming distance is the fraction of bits that disagree in the areas that are not occluded on either image. To account for rotation, comparison between a pair of images involves computing the normalized Hamming distance for several different orientations that correspond to circular permutations of the code in the angular coordinate. The minimum computed normalized. The Hamming distance is the number of bits that disagree. The normalized Hamming distance is the fraction of bits that disagree. Since normalized Hamming distance is used so frequently, many papers simply mention Hamming distance when referring to the normalized Hamming distance. We also follow this trend in subsequent sections of this work. Daugman and Downing [63] describe an experiment to determine the statistical variability of iris patterns. Their experiment evaluates 2.3 million comparisons between different iris pairs. The mean Hamming distance between two different irises is 0.499, with a standard deviation of Following on concepts developed by Daugman, they consider the probability of bit values in an iris code and the Hamming distance between iris codes to develop an analytical model of the false reject rate and false accept rate as a function of the probability p if a bit in the iris code being flipped due to noise. The Hamming distance is used to compute how many bits are matched between two bit patterns. Literature review on Iris recognition system 25

13 Using the Hamming distance of two bit patterns, a decision can be made as to whether the two patterns were generated from different irises or from the same one. In comparing the bit patterns X and Y using the Hamming distance technique, HD, is defined as the sum of disagreeing bits (sum of the exclusive-or between X and Y) over N, the total number of bits in the bit pattern. The weighted Euclidean distance (WED) is used mostly to compare two different templates especially if the template is composed of integer values. The weighting Euclidean distance gives a measure of how similar a collection of values are between two templates. This metric is employed by Zhu et al. [69]. Wildes [20] used the normalized correlation between the acquired and database representation for goodness of match. Normalized correlation is advantageous over standard correlation, since it is able to account the local variations in image intensity that corrupt the standard correlation calculation. 2.6 Review of Methods for Iris Image Quality Assessment As in other biometric systems, the assessment of the image quality is essential for iris recognition, whereas the iris is said to be the most reliable trait with a low false accept rates compared to other biometric modalities [3]. However, recent studies have reported surprisingly high false reject rates (FRRs) due to poor quality iris images (e.g., 11.6% [25], 6% [26]). Also the poor quality images may lead to the failure-toenrol problems. Daugman affirms in [63] that the defocus of an image can be modeled by convolution of an in-focus image by a 2-D point-spread function of the defocus optics such as Gaussian. Deeper analysis reveals that the effect of the defocus is to attenuate the highest frequencies in the image. Therefore, in order to measure the degree of the defocus, Daugman quantify the energy of high spatial frequencies over the whole image through convolving the iris image by the 8x8 high-pass convolution kernel then compute the power of the resulted image to conclude about the defocus level. This method is fast, simple in implementation and no segmentation is needed beforehand. However, only defocus factor is assessed whereas others can also degrade the quality of iris images. Literature review on Iris recognition system 26

14 Wei, et al [67] assesses the iris image quality by taking into account three factors: defocus, motion blurriness and occlusion. To evaluate the defocus score, they again measure the high frequency power of the image by proposing another 5x5 convolution kernel that has a similar shape to the Daugman s but a higher central frequency and larger bandwidth For the motion blurriness, the difference between every two rows of pixel is adopted as a measurement. Finally, the occlusion is assessed by considering the average of the intensity values in the regions. Based on the difference of Fourier spectra between high quality and low quality images, Ma, et al [35] assess three impact factors, namely defocus, motion blurriness and occlusion, by analyzing the frequency distribution of the iris image. Iris images at different quality will result in different distribution of the Fourier spectra. During the last decade, many works have been interested in the evaluation of the quality of an iris image. Despite the diversity of the applied methods, they proved experimentally an improvement of the recognition system performance. Most of these works defined the quality in terms of texture clarity, focus degree, occlusion rate, dilation degree, view angle, etc. The used techniques can be classified into 3 categories: those operating in the Fourier Domain, those based on 2D wavelets transform and the statistical methods Quality Measures in the Frequency Domain : The choice of indices quality measurement in the frequency domain is justified by the fact that an out-of-focus image can be considered as a result of the filtering of the ideal image by a low pass filter, and then the main part of information of texture is located in the low frequencies. On the contrary, this information is between the low and high frequencies for clear image. Daugman estimated the clarity of iris image in term of the rate of the energy of high components. This energy increases proportionally to the degree of focus image (Daugman, 2004). Ma et al. analyzed the frequency distribution of the two areas of size 64x64 pixels around the pupil. Then, the quality indices are used by a SVM classifier for the training and the classification of images in 4 categories: clear images, out-of-focus images, blurred images due to Literature review on Iris recognition system 27

15 the eye movement during the acquisition and images altered by the presence of eyelids and eyelashes [35]. Later, Kalka et al.[68] studied many other factors on the system performance such as: the occlusion, the pupil dilation, the illumination, the percentage of significant pixels, movement of eye, the reflections, the view angle and the distance from the camera. According to their study, the focus, the eye movement and the view angle degrade more the performances. They analyzed the high frequency components to measure the degree of blur due to camera distance and the directional properties of the Fourier spectrum for the blur due to the movement (Kalka et al., 2006). To evaluate the angle of view, they measured the circularity of iris by applying an integro-differential operator to different images obtained by projecting the original image at different angles. So, the angle of correction maximizes this operator Quality measures based on wavelet transform : Generally, the algorithms operating in the frequency domain are applied on the entire image (or an interest region), hence they are sensitive to the noise and give a global sight of the focus degree of the iris texture. To solve these problems, many solutions applied the 2D wavelets transform to produce a local descriptor of the iris quality. Chen et al. [73] suggested a local measure of quality based on the Mexican Hat wavelet transform. The segmented iris image is divided into multiple concentric bands with a fixed width, around the pupil. The degree of blur of each band is measured by the energy of the wavelet coefficients. Then, a global index of quality was defined as a weighted average of the local quality measurements. The weight reflects the distance of the candidate band relative to the pupil. In order to enhance iris image, Vatsa et. al. used a discrete wavelet transform DWT and a SVM classifier on a set of 8 images that incorporates the original iris image and its transformed one by 7 known enhancement algorithms such as the histogram equalization, the entropy equalization, etc. the DWT is applied on each image, and the coefficients of the approximation and details bands are classified as coefficients of good quality by SVM classifier [28-31]. Literature review on Iris recognition system 28

16 2.6.3 Quality measures based on statistical measures : In addition to the techniques described above, several researchers have considered statistical measures to assess the quality of iris images. Zhang et al.[160] filed a patent concerning the process that determines whether the image is focused correctly. It is based on analyzing the shape and the continuity of the iris boundary. They considered a number of lines crossing the pupil boundary, for each line, statistical values are calculated for the pixels belonging either to the pupil and the iris. Proença et. al. [77] developed a method based on statistical measures and neural networks. The process consists in calculating 5 statistical measures in 7x7 windows derived from a segmented polar iris image. The measures commonly used are: ASM (Angular Second Moments), entropy, contrast, energy and inertia. Then, a simple thresholding of index computed in each analysis window permit the classification of central pixel into noisy or significant pixel. To determine whether the image has enough information to identify person, Belcher et al. [85] and Zhou et al. [86] developed a quality index by combining 3 index: the dilation score, the occlusion score and the feature information score. Iris image is segmented then the texture is analyzed by Log-Gabor filters. A global feature information score is estimated by averaging the entropy information distance between pairs of consecutive rows of the filtered image. This quality measure was used by Y. Du et al [85] to evaluate the quality of a compressed iris image. In fact, during compression, iris patterns are replaced by new artificial patterns, and only the most distinctive iris patterns resist. These false patterns are too correlated compared to the original image and they become more important through compression rate. Consequently, the more compression rate is elevated, iris texture quality becomes weaker. Based on the fact that the inner region of iris contain more discriminative patterns, Sung et al. improved the matching performance by merely weighting the inner and outer iris regions with respectively 1 and 0. In the cited works, the quality measures are often computed in the segmented images which make them sensitive to the segmentation errors. In practice, no segmentation method has a rate of 100% of correct segmentation. Nevertheless, the study of Zhou et al. is among the rare that took into account the evaluation of the Literature review on Iris recognition system 29

17 segmentation results of an iris image. Zhou et. al.[86] evaluated the segmentation accuracy in terms of localizing correctly the center and boundary of pupil and the iris boundary including the limbic boundary and the eyelid boundaries. This measure is based on the analysis of the histogram of a rectangular horizontal area including the two centres, three sub-regions belonging to pupil, iris and sclera. 2.7 Relevance of Proposed Research Work Now a day, most popular methods on texture analysis are multi-resolution or multichannel analysis such as wavelet decomposition and Gabor filters. The wavelet transforms have more advantages than Gabor filters. The disadvantage of Gabor filters is that the output of Gabor filter banks are not mutually orthogonal, which may cause a significant correlation between texture images features. Wavelet and Gabor transform are usually not reversible that restricts their applicability for texture retrieval, but Wavelet transform can overcome some of these disadvantages. Wavelet transform is more superior to Gabor transform. This is because wavelet transform provide a precise and unifying framework for processing of the signal and image at variety of scales. Based on this study, Discrete wavelet transform (DWT) is applied on a set of iris texture images and statistical features such as mean, standard deviation and norm entropy will be extracted from the approximation and detail coefficients of DWT decomposed images, at various scales. The different characteristics of wavelet statistical features (WSF)) extraction method is applied for iris texture image classification by using various wavelet families. All of these features are given to multilayer perceptron artifical neural network (MLPANN) for classification. Wavelet packet analysis is an extension of Discrete wavelet transform (DWT) and it turns out that the DWT is only one of the many possible decompositions that could be performed on the signal. The advantage of wavelet packet analysis is that it is possible to combine the different levels of decomposition in order to achieve the optimum time-frequency representation of the original signal. Wavelet packet neural networks try to combine aspects of Wavelet packet transformations for the purpose of feature extraction and selection with characteristic decision capabilities of neural Literature review on Iris recognition system 30

18 networks approaches. The WPNN is constructed based on wavelet transform theory and is an alternative to feed forward neural networks for pattern classification. An algorithm of back propogation type will be derived by adjusting the parameters of WPNN. Quality of the iris image plays a crucial role in recognition accuracy. The quality of the extracted biometric features is measured and this information is used to constraint the features that are taken into account in the computation of the similarity between iris signatures. The effect of noise regions in the captured irises is very important. The effect of noise is further reduced by division of the segmented and normalized iris image into different regions and performing independent feature extraction and comparison on each region. 2.8 Concluding Remarks This chapter provides a background for iris recognition system. This background illustrates that the iris recognition system consists of four main stages. These stages are iris localization or segmentation, iris normalization, iris feature encoding, and iris template matching. This chapter also gives a literature review on quality measures used in the performance enhancement of the iris image Literature review on Iris recognition system 31