# A New User Dependent Iris Recognition System Based on an Area Preserving Pointwise Level Set Segmentation Approach

- Nakissa Barzegar
^{1}Email author and - M. Shahram Moin
^{1}

**2009**:980159

https://doi.org/10.1155/2009/980159

© N. Barzegar and M. S. Moin. 2009

**Received: **30 September 2008

**Accepted: **11 March 2009

**Published: **26 April 2009

## Abstract

This paper presents a new user dependent approach in iris recognition systems. In the proposed method, consistent bits of iris code are calculated, based on the user specifications, using the user's mask. Another contribution of our work is in the iris segmentation phase, where a new pointwise level set approach with area preserving has been used for determining inner and outer iris boundaries, both exclusively performed in one step. Thanks to the special properties of this segmentation technique, there is no constraint about angles of head tilt. Furthermore, we showed that this algorithm is robust in noisy situations and can locate irises which are partly occluded by eyelid and eyelashes. Experimental results, on three renowned iris databases (CASIAIrisV3, Bath, and Ubiris), show that our method outperforms some of the existing methods, both in terms of accuracy and response time.

## Keywords

## 1. Introduction

The demand for high-confidence authentication of human identity has grown steadily since the beginning of organized society. The identification systems using unique factors of human irises play an important role in this field. In comparison with other biometrics, iris recognition systems have many advantages. Since the degree of freedom of iris textures is extremely high, the probability of finding two identical irises is close to zero; therefore, the iris recognition systems are very reliable and could be used in most secure places [1–3].

A regular iris recognition system consists of different major steps, including image acquisition, iris localization, feature extraction, and matching and classification. In this paper, we have used standard iris datasets; therefore, we have not focused on the image acquisition phase. Other parts of an iris recognition system will be discussed later.

One of the most important steps in iris recognition systems is iris localization, which is related to the detection of the exact location and contour of iris in an image. Obviously, the performance of the identification system is closely related to the precision of the iris localization step [1, 2]. For iris localization, existing methods mainly use circular edge detectors or other standard image processing techniques, to detect the iris location based on derivative operators, which calculate the sum of gray level differences on the vertical arc. It must be mentioned that, since the upper and lower parts of the outer iris boundary are usually obstructed by eyelids, it could be impossible to use a complete circle, instead of two vertical arcs, to represent the iris boundaries. In these methods, the result of localization algorithm depends on the tilt angle of the iris and the quality of the boundaries [1, 2, 4]. For example, if some parts of boundaries are occluded by the eyelid and eyelashes, performance of these algorithms reduces considerably and even in some cases, they fail. Another source of error is the presence of other parts of face in input image.

In [1], Daugman introduces a circular edge detection operator for iris localization, which tries to find a circle in the image with maximum gray level differences with its neighbors. In its method, thanks to a significant contrast between iris and purple regions, the inner boundary is localized. Then, outer boundary is detected using the same operator with different radii and parameters. In order to remove eyelids, Daugman changes the curve of integral to find an arc which accurately detects iris boundaries. As features, he uses the sign of real and imaginary parts of Gabor Wavelet coefficients of iris image. In matching phase, Hamming distance between binary codes of the query iris and irises in database is calculated. In his recent work [5], Daugman proposed four modifications in his algorithm, including (1) using active contour models (Snake model) for iris localization, (2) handling off-axes gaze samples using Fourier-based methods, (3) using statistical methods for detecting eyelashes, and (4) score normalization in large number databases.

An alternative for iris segmentation and localization has been proposed by Camus and Wildes [3], which is based on edge detection operator, followed by Hough transform. This method has a high computational cost, since it searches among all of the potential candidates. For eyelid detection, Wildes uses some constrains to locate the true edge points.

Snake approach has been used for iris localization in [6]. Using this technique, the boundary of the irises is located without any circularity constraint. In [7], an easy to difficult method has been used for iris localization by, first, determining high-contrast parts of boundary, and then, detecting outer boundary and eyelids. It is obvious that, because of their lower SNR, each step is more challenging than previous ones. For exact inner boundary detection, authors used Harr Wavelet transform followed by modified Hough transform. In the next step, outer boundary is localized with integral differential operators. Since the search space for determining the center and radius of inner boundaries could be limited, the speed of the algorithm is considerably improved. In the last step, for detecting eyelids in the image, a method is utilized based on texture segmentation.

Sun et al. [8] proposed iris localization using texture segmentation. First, they use the information of low frequency of Wavelet transform of iris image for pupil segmentation and also localize the iris with a different integral operator. Then, they detect the upper eyelid next to eyelash segmentation. Finally, the lower eyelid is localized using parabolic curve fitting, based on gray level segmentation.

Huang et al. [9] used a new noise removing approach based on the fusion of edge and region information. The whole procedure includes three steps: rough localization and normalization, edge information extraction based on phase congruency, and the infusion of edge and region information. They proceeded to iris segmentation by simple filtering, edge detection, and Hough transform. This method is specifically proposed for removing eyelash and pupil noises. Boles and Boashah [10] and Lim et al. [11] mainly focused on the iris image representation and feature matching without introducing a new method for segmentation.

Tisse et al. [12] proposed a segmentation method based on integro-differential operators with Hough transform. This approach reduces the computation time and excludes potential centers outside of the eye image. Eyelash and pupil noise have not been considered in this method neither.

Kong and Zhang in [13] presented a method for eyelash detection. Separable and multiple eyelashes are detected using 1D Gabor filters and the variance of intensity, respectively. In this work, specular reflection regions in the eye image are localized using a predetermined threshold value. Thornton et al. [14] used a general probabilistic framework for matching patterns of irises, which improves pattern matching performance, when the iris tissue is subject to in-plane wrapping.

Monro et al. in [15] present a novel iris coding algorithms based on differences of Discrete Cosine Transform (DCT) coefficients of overlapped angular patches with normalized iris image. Iris localization is done using the circularity shape of iris boundaries.

Other methods exist for iris localization, including [12, 16]. However the above mentioned techniques are much more cited in literature. There are also a few papers which survey literature in iris recognition subject; amongst them, Bowyer et al. [2] is one of the best.

We have used active contour based-localization method in [4]. In this paper, we improve our method and test its performance on three famous databases, namely, CASIA-IrisV3 [17], Bath [18], and Proença and Alexandre [19]. The results show the superiority of our proposed method in comparison with other methods, including the method proposed in [6], which is also based on geodesic active contour for iris localization. The details will be discussed in Section 2.

In [19], new approaches for localization have been introduced. In their paper, they use a dataset of irises with heterogeneous characteristics, simulating the dynamics of a noncooperative environment. Their method builds a feature set from pixel position and pixel intensity . They apply a fuzzy clustering algorithm to cluster the pixels. In Section 4 we compare our proposed method to their results.

- (1)
Usually, the iris inner and outer boundaries are detected using circle fitting techniques (except the recent works of Daugman [5] and Ross and Shah [6]). This is a source of error, since the iris boundaries are not exactly circles.

- (2)
In almost all of these methods, inner and outer boundaries, eyelashes, and eyelid are detected in different steps, causing a considerable increase in processing time of the system.

- (3)
The results of the circle fitting method are sensitive to the image rotation, particularly if the angular rotation of the input image is more than 10 degrees.

- (4)
In noisy situations, the outer boundary of iris does not have sharp edges.

- (5)
After detecting iris boundaries, the resulted iris area is mapped into a size independent rectangular shape area.

- (6)
None of these methods take into account the user specifications.

- (i)
We use a pointwise area preserving level set approach for iris localization, which guarantees the correct segmentation of iris, even in noisy environment and regardless of the head tilt and occlusion. Although active contours for localization have been also used in [5, 6], our proposed method has many advantages compared to those approaches (we will discuss these advantages in details in Section 2).

- (ii)
We propose a new user dependent method which improves the system recognition performance.

In [4], we explained how to use pointwise level set with area preserving capability for iris localization purposes. We have also introduced a method for mapping the initial coordinates to polar space based on the estimated location of the center of pupil. In this paper, in order to reduce the complexity of the polar mapping calculations, we propose the improved version of the above mentioned method, which is based on the point trajectory of moving contours. We show the results of the new method on CASIA-IrisV3, Bath, and Ubiris datasets.

The rest of the paper is organized as follows. Section 2 briefly describes the theory of pointwise level set approach with area preserving capability. Section 3 is dedicated to the user dependency in iris recognition systems. Experimental results are presented in Section 4 and Section 5 concludes the paper.

## 2. Iris Localization with Pointwise Level Set Approach

In this approach, the moving front is defined as a zero level of a higher dimensional potential function [20]. Consequently, the curve corresponding to the zero level set of this potential function is enabled to handle topological changes, such as splitting and merging. Furthermore, it is not necessary to initialize the algorithm very close to the final contours, which is the case of Snakes model. According to the level set model, the initial curve is deformed using the following evolutionary equation:

A distance measure can be used for initializing the potential function . It means that each point of the three-dimensional potential function is initialized with the minimum distance of that point to the contours. More details on this subject are available in [20]. The evolution of is such that its zero levels movement corresponds to deformation of the initial curve. This evolution may be described by the following equation:

## 3. Template Generating with User Dependency

Gabor filters are traditional choices for obtaining localized frequency information, and thanks to their similarity to the human vision system [1], these filters are vastly used in iris feature extraction phase. However, they suffer from two major drawbacks: (1) the maximum bandwidth of a Gabor filter is limited to approximately one octave, and (2) Gabor filters are not optimal, if one is seeking broad spectral information with maximal spatial localization. Considering these points, we used log Gabor filters [25] for feature extraction. Equation (4) shows this filter:

where is the filter's center frequency. To obtain constant shape ratio filters, the term must also be held constant for different s.

It must be mentioned that using these filters is not an originality of this work (see [26]). Considering the real and imaginary parts of filters, texture of iris could be mapped to the iris codes, and as mentioned in [24], regarding to the distance of bits from axes, it is possible to choose some probability of bit consistency. For each user, the iris code of different samples is calculated, and by comparing these iris codes, the probability of changing each bit is determined. By choosing a threshold, it could be possible to judge about the consistency of each bit. Details about the consistency of bits in the iris codes can be found in [27].

For being rotation invariant, in this phase, like Daugman's method [4], the enrolled iris code will be compared with different shifted test iris codes to find the best match.

## 4. Experimental Results

Ubiris iris database version 1 is composed of 1877 images collected from 241 subjects taken in two sessions (Figure 7(c)). Unlike the CASIA-IrisV3 database, it includes images in different noisy situations, which permits to evaluate the robustness of iris recognition methods in presence of noise [19].

To evaluate the performance of our algorithm, we have used the K-fold cross validation technique. For CASIA-IrisV3 database, for each subject, three-iris samples have been utilized, to extract the user dependent iris code, and the rest of samples to test the algorithm. For Bath database, the number of samples used to extract the code is five. We have repeated this technique in a way that all of the iris images have been used in K-fold cross validation strategy.

In this work, the precise location of an iris is determined using pointwise level set approach with area preserving capability. Generally speaking, active contour models have been used previously in iris recognition systems [6]. Although active contour refers to a family of moving contour methods, in some papers, it corresponds to the Snake techniques [5]. In previous sections, we have described the drawbacks of the Snake model. Geodesic active contours with point correspondence have been used for iris segmentation in [4]. In this paper, we propose a method based on pointwise level set approach with area preserving capability.

The vertical histogram is calculated as follows: size of the vertical histogram is equal to image's height, and the value of each histogram bin is equal to the sum of gray levels of a row of the image. The minimum of this histogram corresponds approximately to the vertical location of the center of inner boundary circle (almost circle). Indeed, pixels located in the pupil region are always dark; therefore, their values are close to 0. Thus, the minimum of the histogram shows the line that has the lowest number of dark pixels, that is, the diameter of the inner boundary circle. The intersection of this line with the output of the horizontal histogram shows the approximate location of the center point (Figure 8). Our experimental results show that we can locate the center of pupil in a point inside the pupil, even for difficult samples having other dark areas in the eye image. For image samples of datasets used in this paper, all pupils are placed almost in the center of the image.

For calculating *d* from the approximate center, one dimensional derivation in the right horizontal axes has been calculated.
is equal to the length of line between the approximate center and some pixels after the found edge (in our experienced *d* could be an integer between 10 and 30):

The proposed one step segmentation approach improves the speed of the whole process in comparison with regular two-step boundary detection methods.

Comparing localization accuracy of different methods using Ubiris database. The whole table entries are taken from reference [19], excluding the last row which contains the results obtained using our approach.

Methodology | Parameters | Session 1, % | Session 2, % | Degradation |
---|---|---|---|---|

Daugman | Original methodology | 6.99 | ||

Daugman | Histogram equalization preprocessing | 4.69 | ||

Daugman | Threshold preprocessing (128) | 1.22 | ||

Wildes | Original methodology | 2.00 | ||

Wildes | Shen and Castan edge detector | 0.82 | ||

Wileds | Zero crossing edge detector | 1.88 | ||

Caumus and Wileds | Original methodology, number of directions = 8 | 7.49 | ||

Martin-Roche et al. | Original methodology | 5.99 | ||

Tuceryan | Total clusters = 5 | 3.56 | ||

Proenca et al. | 0.14 | |||

Our Proposed method | Pointwise level set approach with area preserving capability | 0.1 |

We tested our localization algorithm on Ubiris dataset and compared the results with the results published in [19]. The results in [19] were obtained by visual inspection of each segmented image. Although this is not the best for meaningful comparison, we did the same for localization evaluation in our system. Table 1 shows these results that are the proof of performance of our algorithm even for poor quality images. Indeed, in term of the degradation, the lowest accuracy degradation in the presence of noise belongs to our method, depicting low sensitivity of our approach to the image condition.

### 4.1. Error Definition

In order to measure the error of our method, we compared the points of the detected boundaries with those of the real boundaries. First, the exact boundary contours for inner and outer parts of irises are determined point to point manually. Then, the sum of the distance between the interface points and their nearest point in the correct boundary is calculated. Total error of localization is estimated using

### 4.2. Response Time

### 4.3. Hamming Distance

### 4.4. ROC Curves

As it can be seen, our method is robust against rotation, while rotation degrades the performance of other methods considerably, due to their circular edge detection nature. In general, circular edge detection process is based on determining the location of the circle with maximum differences of pixel gray levels for two adjacent circular curves. In practice, these differences are calculated using two arches, instead of a whole circle. The performance of the iris localization depends on the location and angle of these arches in relation with the iris axis, and, as a consequence, rotating the image degrades the results of circular edge detection, mainly due to wrong arches used in the process and presence of eyelid and eyelashes. In contrast with these conventional methods, the iris localization in the proposed method is based on geodesic active contour model, which calculates the iris boundaries independently to any geometric shape, including circles and arches; therefore, it is robust to the image rotation problem.

## 5. Conclusions

We have proposed a new user-dependent iris recognition method. Using a specific mask for each user, inconsistent bits of iris code are omitted during the Hamming distance comparison phase. As the experimental results show, using this approach, the performance of the whole system is improved considerably. Another contribution of this paper is the utilization of pointwise level set approach with area preserving capability for iris segmentation and localization. In this algorithm, the exact location of the iris can be detected using an iterative algorithm based on the active contour model. Comparing our algorithm with other methods, we showed that the new approach is able to solve some of the previous method's drawbacks. For instance, using our method, the iris location can be detected regardless to its angular position and shape, and this is done in only one step. Also, previous methods usually detect iris boundaries using circular edge. One of the disadvantages of this approximation is its sensitivity to the rotation of the iris images. In recent years, active contour model have been used for iris detection purposes. However, our method has some advantages over other methods. Indeed, an area preserving algorithm is used to compensate the problem of incorrect iris boundary detection in presence of noise. Furthermore, even when eyelids occlude some part of iris, our algorithm localizes iris area properly [4]. The experimental results show that our method outperforms the current methods both in terms of accuracy and response time.

## Authors’ Affiliations

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