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Page 1: [IEEE 2008 First Workshops on Image Processing Theory, Tools and Applications (IPTA) - Sousse, Tunisia (2008.11.23-2008.11.26)] 2008 First Workshops on Image Processing Theory, Tools

Iris Recognition using Steerable Pyramids

Nefissa Khiari, Hela Mahersia, Kamel Hamrouni Laboratory of Signal Processing, Image Processing and Pattern Recognition,

National Engineering School of Tunis, University of Tunis El Manar, BP-37 Le Belvédère 1002 Tunis, Tunisia

Abstract This work presents a new iris recognition method based on steerable pyramid transform. This method consists of four steps: localization, normalization, features extraction and matching. After locating the iris boundaries by Hough Transform, normalization is operated by unwrapping the circular ring and isolating the noisy regions. Steerable pyramid filters are then used to capture orientation details from the iris texture. The features are extracted on each filtered sub-image to form a fixed length feature vector which will be compared to other vectors in the matching step. This technique has been tested on infrared light iris images. It has been compared, in both identification and verification modes, to known methods. Key Words- Image processing, Biometry, Iris recognition, Steerable pyramid. 1. Introduction 1.1. Overview

Nowadays, security requirements are deeply increasing because of the incessant fraud attempts. Biometric systems are considered to be the ultimate solution to this problem offering a reliable way to authenticate the identity of a person based on physiological, behavioral or biological characteristics. The authentication systems based on iris recognition are reputed to be the most reliable and accurate biometric modality, since the probability of finding two people having an identical iris pattern is 10-72 [1]. 1.2. Background

Many works on iris recognition have been done since the 90’s, but the first work realized by Pr. John Daugman remains the fundamental reference as almost iris recognition systems that can be found on the market are based on his studies. The Daugman’s method is based on localization by integro-differential operators and a characterization using Gabor filters [2]. Later, Wildes

proposed another method based on Hough transform for locating the iris and Laplacien Pyramid for characterization [3]. Other works have been then investigated such as Boles’ [7] which is based on zero crossings wavelet transform. Later, Zu [8] operated with two characterization methods: mutichannel Gabor filtering and 2D wavelet transform. In another work, Lim [9] proposed a new system based on Haar wavelet transform to extract the features and neural networks (Learning Vector Quantization) to match iris samples. Afterward, Tissé [10] developed a new method to locate the iris, combining integro-differential operators and Hough transform; characterization was performed using 2D Hilbert transform. Masek [11] proposed another system based on Log-Gabor filtering. Later, Ma [12] presented a system using Circular Symmetric Filters for characterization and an improved Nearest Feature Line method for matching. In the same year, Mellakh [13] conceived a method based on wavelet packet transform. Since then, works are in constant expansion in this research field, and new solutions need to be developed trying to improve biometrical system performance.

Through this work, we aim to introduce a robust method to extract the iris features using the Steerable Pyramids.

1.3. Outline

This paper is composed of four major sections. This first section is dedicated to introduce the iris recognition technology and to summarize some related woks. Section 2 describes in details the proposed method for extracting features and recognizing them. In section 3, experimental results are reported and our method is compared to popular ones in both verification and identification mode. Finally, conclusions and a discussion about future enhancements are provided in the last section 4. 2. Proposed method

The different processing blocks of our method are as previously presented: 1) iris localization 2) iris normalization 3) features extraction and 4) iris matching.

Image Processing Theory, Tools & Applications

978-1-4244-3322-3/08/$25.00 ©2008 IEEE

Page 2: [IEEE 2008 First Workshops on Image Processing Theory, Tools and Applications (IPTA) - Sousse, Tunisia (2008.11.23-2008.11.26)] 2008 First Workshops on Image Processing Theory, Tools

2.1. Iris localization

The captured image contains not only the iris texture, but also some noisy parts like pupil, eyelids and eyelashes. To delimitate the region of interest, we need, first, to locate the inner and outer boundaries of the iris. These boundaries are approximated to be two non co-centric circles that can be determined using the Hough transform. Two steps are required: (1) find the candidate points that might belong to the circle. (2) Take the circle that gathers the maximum of votes in the parametrical space of circles. Candidate points are detected by applying vertical and horizontal gradients for the inner boundary and by applying only vertical gradients for the outer boundary in order to minimize the effect of the eyelids and eyelashes. 2.2. Iris normalization

In the acquisition step, an iris is usually not in the same position on the captured images, which is due to head slopes even with trained subjects. To solve this problem, we rotate the iris ring obtained in the localization step into 5 directions. In this way, the proposed system becomes invariant to rotation.

But there are still other problems that face the recognition procedure, like the non co-centricity of the iris and the pupil, the eye-to-camera distance variation, and the illumination variation that cause elastic deformations of the iris texture. All these effects might interfere with the results of the pattern matching. To compensate the stretching of the iris texture and break the non co-centricity of the iris and the pupil, J.Daugman suggested a cartesian-to-polar reference transform that maps the iris ring into a fixed-sized rectangular block of texture [2]. Figure 1 illustrates the iris pattern obtained after unwrapping the circular ring. The system is then scale invariant.

Figure 1. Iris rectangular representation (64x512). As shown in Figure 1, the normalized iris patterns are

still affected by eyelids and eyelashes. A simple technique of thresholding has been used to minimize the effect of the eyelashes, whereas the noisy region including the eyelids has been identified as comprised between an angular interval of [Π/4 3Π/4] and [5Π/4 7Π/4] and having a width of half the radius of the iris ring. The

region of interest (ROI) is then obtained as illustrated in figure 2.

Figure 2. Delimitation of the region of interest: (a) original image; (b) detected region of interest; (c) relative unwrapped

pattern. 2.3. Features extraction

The iris is composed of many irregular small blocks randomly distributed around the pupil, such as freckles, stripes, furrows and crypts [2]. This structure involves abundant textural information that needs to be extracted using methods able to capture both the frequency and the orientation information of the iris. We thus propose an effective method to capture this discriminating information: The Steerable Pyramid [5]. This transform combines multi-scale decompositions with differential measurements, which perfectly suits our application.

Steerable Pyramid. The steerable pyramid, introduced by Simoncelli & Freeman [5], is a linear multi-scale multi-orientation decomposition that provides a front-end to many image-processing applications particularly in texture analysis.

The basis functions of a steerable pyramid are directional derivative operators that come in different sizes and orientations. The pyramid can be designed to produce any number of orientation bands k. The resulting transform is overcomplete by a factor of 4k/3. The representation is translation invariant (it is aliasing free) and rotation invariant (the subbands are steerable). More importantly, the transform is a tight-frame, specifically, the same filters used in the decomposition are used for the reconstruction.

The block diagram of a steerable pyramid [6] is given in figure 3 for both analysis and synthesis. In the analysis part, the image is decomposed into highpass and lowpass subbands using H0 and L0 filters. The lowpass band continues to break down into a collection of oriented bandpass subbands B0, B1, …, Bn-1 and a lower lowpass subband L1. The lower lowpass subband is subsampled by a factor of 2 in the x and y directions. This process constitutes the first level of decomposition of a steerable pyramid. Repeating the enclosed area on the output of

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subsampling provides the recursive (pyramid) structure, hence the next levels. In the synthesis part, the reconstructed image is obtained by upsampling the lower lowpass subband by a factor of 2 and adding up the collection of bandpass subbands and the highpass subband.

The steerable pyramid is best defined in the Fourier domain where it provides a polar-separable decomposition, thus allowing independent representation of scale and orientation.

Figure 3. First level of the diagram system of a steerable pyramid.

Fig. 4. Idealized illustration of the spectral decomposition performed by a steerable pyramid with 4 orientations.

Frequency axes range from –Π to Π.

Figure 4 shows the idealized frequency response of the subbands, for k = 4. The Fourier magnitude of the ith oriented band-pass filter is given in polar-separable form as: (1)

Where: is the angular component, with and ; and is the radial component.

The radial decomposition is constrained as follows : 1. Bandlimiting for aliasing cancellation:

for (2)

2. Unity system response amplitude to avoid amplitude distortions:

(3)

3. Recursion relationship: (4)

The angular decomposition is constrained by the

property of steerability. A set of filters form a steerable basis if (i) they are rotated copies of each other and (ii) a copy of the filter at any orientation may be computed as a linear combination of the basis filters. The simplest example of a steerable basis is a set of k+1 kth order directional derivatives. This condition can be expressed as: (5) where , for and

The constraint in equation (5) states that is the kth order directional derivative in direction , of the function [4].

Figure 5 illustrates a three level steerable pyramid decomposition of an eye image, with k=3. Shown are the four orientated bandpasss images at three scales and the final lowpass image. The initial highpass image is not shown.

Feature vectors. It is important to point that only the analysis part of the steerable pyramid diagram system is applied while extracting features from the iris texture.

Figure 5. eye image decomposition using a 3-level steerable pyramid with 4 orientations (third derivative).

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Page 4: [IEEE 2008 First Workshops on Image Processing Theory, Tools and Applications (IPTA) - Sousse, Tunisia (2008.11.23-2008.11.26)] 2008 First Workshops on Image Processing Theory, Tools

A one-level fifth-derivative steerable pyramid (6 orientations) is operated to filter each (16x16) bloc of the (64x512) rectangular iris pattern obtained after the normalization step. The energy is then calculated on each filtered bloc, giving consequently a 128-length feature vector of real numbers (energies) for each iris pattern (Figure 6).

Figure 6. Features exctraction from an iris pattern. 2.4. Iris matching

The final step performs the Cityblock distance test, which is generated from the feature vector y developed from the candidate and from the feature vector x already stored in the database. (6) 3. Experimental results

We evaluated the performance of our algorithm on infrared light images. And to get more accurate evaluation, we compared our method to the L.Masek’s one [11] in both identification and verification modes. We also carried out a study about the influence of the different parameters on the system so as to determine the optimum parameters producing the highest recognition rates. 3.1. Database

We used the CASIA Iris Image Database acquired from the National Laboratory of Pattern Recognition (NLPR), Institute of Automation (IA), Chinese Academy of Sciences (CAS).

The Database contains 756 infrared iris images from 108 different eyes of 80 subjects. The images were captured in two sessions of one month interval, which is a real-world application simulation.

The images used in identification were acquired from 50 eyes, 7 images per eye, leading to 760’585 matches between the 128 length feature vectors. Whereas in verification, the same images were used as authentic : 3 images to the learning stage and 4 to the tests, plus impostor iris images taken from 14 other eyes, 7 images per eye, leading to 873’160 matches : 280'875 in the learning phase and 592'285 in the test one.

3.2. Results

Since our work focuses on characterization, we chose to test separately Localization and Features extraction steps, so as to have more relevant results. The whole Casia database was used to test the localization step but, only the iris images giving successful localization were kept to test the features extraction method with steerable pyramid. Localization. We adapted the opensource code provided by Libor Masek, for Hough transform segmentation, to our algorithm. 88.1% of successful localization was achieved. It is worth noticing that the inner boundary was always well detected. Outer boundary bad localizations were most of the time due to narrow visible area of the iris ring, or to abundant eyelashes overlaps (Figure 7).

Figure 7. Samples of successful (left) and bad (right) iris localizations.

System Parameters. The proposed method includes a number of parameters that need to be fixed. These parameters concern the filter used in the pretreatment phase (Table 1), the normalization parameters (number of rotations, unwrapping the iris ring, ROI angle, contribution of the noisy region in calculations (Table 6)), the characterization parameters and the type of the computed distance (Table 7).

In the characterization phase, we varied the size of the blocs from (64x64), (32x32), (16x16) to (8x8). We also studied the impact of the steerable pyramid parameters -i.e. the number of scales n (n∈{1,2,3}) and the number of rotations (k+1) ((k+1)∈{0,2,4,6}). Then we tried to get the influence of the extracted feature, so we estimated the energy, the standard deviation and the entropy. But still another factor had to be fixed: the feature computation region. In fact, we had to choose between computing the feature on the whole pyramid of each filtered bloc, and computing the feature on each subband of the pyramid of each filtred bloc.

We made a large number of tests; in each one, we

studied the influence of one parameter and we fixed all the rest. Tables 1 to 7 show some of the results. The tests were run first in identification mode, and then the

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Page 5: [IEEE 2008 First Workshops on Image Processing Theory, Tools and Applications (IPTA) - Sousse, Tunisia (2008.11.23-2008.11.26)] 2008 First Workshops on Image Processing Theory, Tools

optimum parameters were kept to test the verification mode.

The best recognition rates were obtained with the following optimum parameters: no pretreatment stage; the normalization was performed with 5 rotations, using an ROI angle of pi/4 and the noisy regions were replaced by a specific mean; in features extraction, the energy was computed on the whole pyramid of each filtered (16x16) bloc using a one-level (1scale) and six-orientation (k+1=6) Steerable pyramid filter; the matching process was completed by means of the cityblock distance.

Identification. As mentioned previously, the influence of the parameters has been studied in identification mode. With the optimum parameters, the system reached 98.47% of successful identification.

Table 1. Identification rates according to the pretreatment.

Table 2. Identification rates according to the size of blocs.

Table 3. Identification rates according to the extracted feature.

Table 4. Identification rates according to the number of scales of the steerable pyramid.

Table 5. Identification rates according to the number of rotations of the steerable pyramid (1 scale, (16x16) blocs).

Table 6. Identification rates according to the contribution of the noisy region into calculations.

Table 7. Identification rates according to the distance.

Learning. The learning phase allows computing the minimum and maximum thresholds in order to get the optimum threshold (criterion of decision). In case of overlap, the minimum threshold corresponds to the minimum interclass distance, whereas the maximum threshold corresponds to the maximum intraclass distance. We estimated the False Acceptance Rate (FAR) and the False Reject Rate (FRR) of our recognition system. Figure 8 shows the distributions of the two types of populations: “authentic” and “impostors”.

The overlap between the distributions (figure 9) corresponds to a risky zone where an authentic could be rejected (FRR) and an impostor could be accepted (FAR). Our aim is to seek the threshold that minimizes these two error rates which are inverse proportional. The optimum threshold (bright blue) is usually comprised between the minimum and maximum thresholds (purple).

Verification. The ROC curve in figure 10 measures the accuracy of iris matching process showing the FRR and FAR variation according to the decision criterion.

The optimum threshold is located at 0.61 implying 1.08% of false reject and 0.29% of false acceptance (98.63% of good verification).

Figure 8. Authentic (pink) and impostors (blue)

distributions of the proposed system: (a) Authentic distribution; (b) Impostors distribution.

(b) (a)

Page 6: [IEEE 2008 First Workshops on Image Processing Theory, Tools and Applications (IPTA) - Sousse, Tunisia (2008.11.23-2008.11.26)] 2008 First Workshops on Image Processing Theory, Tools

Figure 9. Illustration of the overlap between the two distributions.

Figure 10. ROC curve of the proposed system ( with optimum parameters).

3.4. Comparison with existing methods

Since works related to iris recognition were not realised on a unified database, and since rates provided in the literature were not obtained at the same stages of the recognition process, comparing these works seems not to be trivial. Table 8 shows results of some known methods. It has to be emphasized that thanks to the opensource code provided by L.Masek [11], we were able to compare our method to his, using the same database image and the same testing process.

In his method, Masek applied the (circular) Hough transform to locate the iris. To normalize the iris patterns, the circular ring was unwrapped to get a fixed-size iris code, and the eyelids boundaries were detected by means of linear hough transform. To encode the features, Log-Gabor filter was applied on the whole rectangular iris pattern; the filtered image is then phase quantized (4 quadrants Daugman’s encoding method). Matching was operated by measuring the Hamming distance between the iris codes. The comparison results between the two methods reveal that, in verification mode, the L.Masek’s method is slightly better than ours, whereas, in identification, our method proved to be more efficient.

Table 8. Reported results from some popular algoritms.

4. Conclusion

In this paper, we have presented a new algorithm for iris recognition in both identification and verification modes. After locating the iris ring by Hough transform and after normalizing the iris pattern, the proposed algorithm uses a Steerable Pyramid filter to extract features from the iris texture. Each iris pattern is then represented with a fixed length feature vector. Matching is finally processed using the Cityblock distance. Experimental results show that the proposed method is efficient and promising when compared to other existing methods. But, still a lot of improvements need to be accomplished, to be efficient in real-time applications. The most pressing ones are to develop a new localization method and to optimise the source code developed in Matlab language. In addition, elaborating a source code in C language would be better to guarantee a greater portability. Also, complexity and run-time estimations would be useful to evaluate the system performance. Acknowledgement The authors thank the National Laboratory of Pattern Recognition (NLPR), Institute of Automation (IA), Chinese Academy of Sciences (CAS) for their supply of CASIA Iris Image Database. They also thank L.Masek for providing an open source code of his method. References [1] J.G.Daugman, C. Dowing “Epigenetic randomness, complexity and singularity of human iris patterns”, Proc. The Royal Society, vol. 268, pp.1737-1740, London, 2001. [2] J.G. Daugman “High Confidence Visual Recognition of Persons by a Test of Statistical Independence”, Pattern Analysis and Machine Intelligence, IEEE Trans., vol.15, no.11, pp.1148-1161, November 1993. [3] R.P. Wildes, “Iris Recognition: An Emerging Biometric Technology”, Proc. of the IEEE, vol. 85, no. 9, pp. 1348 – 1363, September 1997.

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[4] W.T. Freeman, E.H. Adelson, “The Design and Use of Steerable Filters”, IEEE, Pattern Analysis and Machine Intelligence (PAMI), vol. 13, pp.891-906, 1991. [5] E.P. Simoncelli and W.T. Freeman, “The Steerable Pyramid : A Flexible Architecture For Multi-scale Derivative Computation”, 2nd IEEE Inter. Conf. on Image Processing (ICIP), DC, vol.3, pp.444-447, Washington , October 1995. [6] A. Krasaridis and E.P. Simoncelli, “A Filter Design Technique for Steerable Pyramid Image Transforms”, IEEE, Inter. Conf. on Acoustics, Speech and Signal Processing (ICASSP), vol.4, pp.2387-2390, Atlanta, May 1996. [7] W.W. Boles and B. Boashash, “A Human Identification Technique Using Images of the Iris and Wavelet Transform”, IEEE Trans. on Signal Processing, vol. 46, pp. 1185-1188, April 1998. [8] Y. Zhu, T. Tan and Y. Wang, “Biometric Personal Identification Based on Iris Patterns”, Proc. of theInter. Conf. Pattern Recognition (ICPR), vol.2, pp.801-804, Spain, 2000.

[9] S. Lim , K. Lee, O. Byeon, and T. Kim, “Efficient Iris Recognition through Improvement of Feature Vector and Classifier”, ETRI Journal, vol.23, no.2, pp.1-70, June 2001. [10] C.L. Tissé, L. Martin, L. Torrès , M. Robert, “Person identification technique using human iris recognition”, Proc. 15th Inter. Conf. Vision Interface , pp.294–299, Canada, May 2002. [11] L. Masek, “Recognition of Human Iris Patterns for Biometric Identification”, Bachelor of Engineering Degree Thesis, The University of Western Australia, Australia, 2003. [12] L. Ma, T. Tan, Y. Wang and D. Zhang, “Personal Identification Based on Iris Texture Analysis”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, pp. 1519-1533, 2003. [13] A. Mellakh, K. Hamrouni, E. Krichen, “ Méthode d’identification de l’iris par la transformée par paquet d’ondelettes ”, IEEE, 3rd Inter. Conf Sciences of Electronic, Technologies of Information and Telecommunications (SETIT), Tunisia, 2004.