Abstract
Iris recognition systems have been proposed by numerous researchers using different feature extraction techniques for accurate and reliable biometric authentication. In this paper, a statistical feature extraction technique based on correlation between adjacent pixels has been proposed and implemented. Hamming distance based metric has been used for matching. Performance of the proposed iris recognition system (IRS) has been measured by recording false acceptance rate (FAR) and false rejection rate (FRR) at different thresholds in the distance metric. System performance has been evaluated by computing statistical features along two directions, namely, radial direction of circular iris region and angular direction extending from pupil to sclera. Experiments have also been conducted to study the effect of number of statistical parameters on FAR and FRR. Results obtained from the experiments based on different set of statistical features of iris images show that there is a significant improvement in equal error rate (EER) when number of statistical parameters for feature extraction is increased from three to six. Further, it has also been found that increasing radial/angular resolution, with normalization in place, improves EER for proposed iris recognition system.
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References
Giot R, Hemery B and Rosenberger C 2010 Low cost and usable multimodal biometric system based on keystroke dynamics and 2-D face recognition. In: Proceedings of twentieth IEEE international conference on pattern recognition, 23–26 August 2010, pp. 1128–1131
Cao K, Eryun L and Jain A K 2014 Segmentation and enhancement of latent fingerprints: A coarse to fine ridge structure dictionary. IEEE Trans. Pattern Anal. Mach. Intell. 36(9): 1847–1859
Senoussaoui M, Kenny P, Stafylakis T and Dumouchel P 2014 a study of the cosine distance-based mean shift for telephone speech diarization. IEEE Trans. Audio, Speech Language Process. 22(1): 217–227
Daugman J 1993 High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15: 1148–1161
Daugman J 2004 How iris recognition works? IEEE Trans. Circuits Syst. Video Technol. 14(1): 21–30
Ko Jong Gook, Gil Yeon Hee and Yoo Jang Hee 2006 Iris recognition using cumulative sum based change analyses. International symposium on intelligent signal processing and communication system, pp. 275–278
Kyaw Khin Sint Sint 2009 Iris recognition system using statistical features for biometric identification. In: Proceedings of international conference on electronic computer technology. pp. 554–556
Bansal A, Agarwal R and Sharma R K 2010 Trends in iris recognition algorithm. In: Proceedings of IEEE Fourth Asia international conference on mathematical/analytical modeling and computer simulation, pp. 337–340
He Zhaofeng, Tan Tieniu, Sun Zhenan and Qiu Xianchao 2009 Toward accurate and fast iris segmentation for iris biometrics. IEEE Trans. Pattern Anal.Mach. Intell. pp. 1670–1684
Kumar A and Passi A 2010 Comparison and combination of iris matchers for reliable personal authentication. Pattern Recognit. 43(3): 1016–1026
Li Su, Qian Li and Xin Yuan 2011 Study on algorithm of eyelash occlusions detection based on endpoint identification. In: Proceedings of third international workshop on intelligent systems and applications (ISA), pp. 1–4
Bodade R M and Talbar S N 2009 Shift invariant iris feature extraction using rotated complex wavelet and complex wavelet for iris recognition system. Proceeding of seventh international conference on advances in pattern recognition, pp. 449–452
Tisse C, Martin L, Torres L and Robert M 2002 Person identification technique using human iris recognition. In: Proceedings of international conference on vision interface, Canada. pp. 294–299.
Tsai Chung Chih, Lin Heng Yi, Taur Jinshiuh and Tao Ching Wang 2012 Iris recognition using possibilistic Fuzzy matching on local features. IEEE Trans. Syst. Man Cybern. – B: Cybern. 42(1): 150–162
Vivek S A, Aravinth J and Valarmathy S 2012 Feature extraction for multimodal biometric and study of fusion using Gaussian mixture model. In: Proceedings of the international conference on pattern recognition, informatics and medical engineering. pp. 387–392
Sundaram R M and Dhara B C 2011 Neural network based iris recognition system using Haralick features. In: Proceeding of IEEE third international conference on electronics computer technology pp. 19–23
He Y, Feng G, Hou Y, Li L and Tzanakou E M 2011 Iris feature extraction method based on LBP and chunked encoding, IEEE seventh international conference on natural computation, pp. 1663–1667
Rahulkar A D and Holambe R S 2012 Half-Iris feature extraction and recognition using a new class of biorthogonal triplet half-band filter bank and flexible k-out-of-n: A post classifier, IEEE Trans. Information Forensics Security. 7(1): 230–240
Costa R M D and Gonzaga A 2012 Dynamic features for iris recognition. IEEE Trans. Syst. Man Cybern. – B: Cybern. 42(4): 1072–1082
Ballard D H 1981 Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognit. 13(2): 111–122
Wildes R, Asmuth J, Green G, Hsu S, Kolczynski R, Matey J and McBride S 1994 A system for automated iris recognition. In: Proceedings of IEEE workshop on applications of computer vision. pp. 121–128
Kong W and Zhang D 2001 Accurate iris segmentation based on novel reflection and eyelash detection model. In: Proceedings of international symposium on intelligent multimedia, video and speech processing. pp. 263–266
Ma L, Wang Y and Tan T 2002 Iris recognition using circular symmetric filters. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. pp. 414–417
Zhu Yong, Tan Tieniu and Wang Yunhong 2000 Biometric personal identification based on iris patterns. In: Proceedings of the IEEE international conference on pattern recognition. pp. 2801–2804
Huang J, Wang Y, Tan T and Cui J 2004 A new iris segmentation method for recognition. In: Proceedings of the seventeenth international conference on pattern recognition, vol. 3 pp. 554–557
Bansal A, Agarwal R and Sharma R K 2012 FAR and FRR based analysis of iris recognition system. IEEE international conference on signal processing, computing and control (ISPCC’12), pp. 1–6
Masek L 2003 Recognition of human iris patterns for biometric identification. Technical report, School of Computer Science and Software Engineering, University of Western Australia
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Bansal, A., Agarwal, R. & Sharma, R.K. Statistical feature extraction based iris recognition system. Sādhanā 41, 507–518 (2016). https://doi.org/10.1007/s12046-016-0492-9
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DOI: https://doi.org/10.1007/s12046-016-0492-9