Two Approaches Based on Genetic Algorithm to Generate Short Iris Codes

Two Approaches Based on Genetic Algorithm to Generate Short Iris Codes

Автор: Hamed Ghodrati, Mohammad Javad Dehghani, Habibolah Danyali

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 8 vol.4, 2012 года.

Бесплатный доступ

This paper has the following contributions in iris recognition compass: first, novel parameters selection for Gabor filters to extract the iris features. Second, due to iris textures randomness and assigning the Gabor parameters by pre-knowledgeable values, traditionally, a large Gabor filter bank has been used to prevent losing the discriminative information. It leads to perform extracting and matching the features heavily and on the other hand, the generated feature vectors are lengthened as required for extra storage space. We have proposed and compared two different approaches based on Genetic Algorithm to reduce the system complexity: optimizing the Gabor parameters and feature selection. Third, proposing a novel encoding strategy based on the texture variations to generate compact iris codes. The experimental results show that generated iris codes by optimizing the Gabor parameters approach is more distinctive and compact than ones based on feature selection approach.

Еще

Iris recognition, Feature Selection, Feature Extraction, Gabor-wavelet, Optimization, Genetic Algorithm

Короткий адрес: https://sciup.org/15010297

IDR: 15010297

Список литературы Two Approaches Based on Genetic Algorithm to Generate Short Iris Codes

  • Flom, L, Safir, A. Iris Recognition System. United States Patent No. 4,641,349, Washington D.C.: U.S. Government Printing Office, 1987.
  • Daugman, J.G. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell, 1993, 15: 1148-1161.
  • Daugman, J G. How iris recognition works. IEEE Trans Circuits and Systems for Video Technology. 2004, 14: 21–30.
  • Wildes, R P. Iris recognition: an emerging biometric technology. Proc. IEEE, 1997, 85: 1348–1363.
  • Boles, W, Boashash, B. A human identification technique using images of the iris and wavelet transform. IEEE Trans. Signal Process. 1998, 46: 1185–1188.
  • Tisse, C, Martin, L, Torres, L, Robert, M. Person identification technique using human iris recognition. Vis. Interface Conf., 2002, 294–299.
  • Ali, J M H, Hassanien, A E. An iris recognition system to enhance e-security environment based on wavelet theory. Adv Model Opt. 2003, 5: 93–104.
  • Lim, S, Lee, K, Byeon, O, Kim, T. Efficient iris recognition through improvement of feature vector and classifier. Electronics and Telecommunications Research Institute Journal. 2001, 23: 61–70.
  • Poursaberi ,A, Araabi, B N. Iris Recognition for Partially Occluded Images: Methodology and Sensitivity Analysis. EURASIP Journal on Advances in Signal Processing, 2007, 12 pages.
  • Monro, D M, Rakshit, S, Zhang, D. DCT-based iris recognition. IEEE Trans. Pattern Machine Intell. 2007, 29: 586–595.
  • Miyazawa, K, Ito, K, Aoki, T, Kobayashi, K, Nakajima, H. An effective approach for iris recognition using phase-based image matching. IEEE Trans. Pattern Anal. Machine Intell. 2008, 30: 1741–1755.
  • Azizi, A, Pourreza, H R. A Novel Method Using Contourlet to Extract Features For Iris Recognition System. In: International Conference on Intelligent Computing, Springer Lecture Notes in Computer Science, 2009, 5754: 544-554.
  • Sun, Z, Tan, T. Ordinal measures for iris recognition. IEEE Trans Pattern Anal Mach Intell. 2009, 31: 2211–2226.
  • Sun, Z, Wang, Y, Tan, T, Cui, J. Improving iris recognition accuracy via cascaded classifiers. IEEE Trans. Syst. Man Cyber. 2005, 35(3): 435–441.
  • Thoonsaengngam, P, Horapong, K, Thainimit, S, Areekul,V. Efficient iris recognition using adaptive quotient thresholding. In: International Conference on Biometrics, Springer Lecture Notes in Computer Science, 2006, 3832: 472–478.
  • Liang, H, Cai, Z. Iris Recognition Based on Characters of Iris’s Speckles. World. Cong. Intelligent Control and Automation, 2008, 6793-6797.
  • Hosseini, M S, Araabi, B N, Soltanian-Zadeh, H. Pigment melanin: pattern for iris recognition. IEEE Trans on Instrumentation and Measurement, special issue on Biometrics. 2010, 59(4): 792– 804.
  • Iridian Technologies. Web siteAvailable: http://www.iridian.com/solutions. php [Online] .
  • Lin, Z, Lu, B. Iris recognition method based on the optimized Gabor filters. Int. Cong. Image and Signal Processing, 2010, 1868-1872.
  • Ma, L, Wang,Y, Tan, T. Iris Recognition Based on Multichannel Gabor Filtering. Asian. Conf. Computer Vision, 2002, 279-283.
  • Meng, H, Xu, C. Iris Recognition Algorithms Based on Gabor Wavelet Transforms. IEEE Int. Conf. Mechatronics and Automation, 2006, 1785-1789.
  • Minhas, S, Javed, M Y. Iris Feature Extraction Using Gabor Filter. Int. Conf. Emerging Technologies, 2009, 252-255.
  • Sanchez-Reillo, R, Sanchez-Avila, C. Iris recognition with low template size, In: International Conference on Audio- and Video-Based Biometric Person Authentication, Springer Lecture Notes in Computer Science, 2001, 2091: 324–329.
  • Wei-qi, F, Wang-lan, L, Li, K. Parameter Selection of Gabor Filter Used in Iris Recognition. OPTO- ELECTRONIC ENGINEERING, 2008, 35(8).
  • Yu, L, Zhang, D, Wang, K. The relative distance of key point based iris recognition. Pattern Recog. 2007, 40: 423 – 430.
  • Zheng, H, Su, F. An Improved Iris Recognition System Based On Gabor Filters. IEEE Int. Conf. Network Infrastructure and Digital Content, 2009, 823-827.
  • Nadia, F, Hamrouni, K. An Efficcient and Reliable Algorithm for Iris Recognition Based On Gabor Filters. Int. Conf. Systems, Signal and Devices, 2009, 1-6.
  • Chou, C T, Shih, S W, Chen, D Y. Design of Gabor Filter Banks for Iris Recognition. Int. Conf. Intelligent Information Hiding and Multimedia Signal Processing, 2006, 403-406.
  • Tsai, C C, Taur, J S, Tao, C W. Iris Recognition Using Gabor Filters Optimized by the Particle Swarm Technique. IEEE Int. Conf. Syst. Man and Cyber, 2008, 921-926.
  • Ma, L, Tan, T, Wang, Y, Zhang, D. Personal Identification Based on Iris Texture Analysis", IEEE Trans Pattern Anal Mach Intell. 2003, 25: 1519-1533.
  • Nabti, M, Bouridane, A. An Effective Iris Recognition System Based On Wavelet Maxima and Gabor Filter Bank. Int. Symp. Signal Processing and its Applications, 2007, 1-4.
  • Holligsworth, K P, Bowyer, K W, Flynn, P J, 2009. The Best Bits in an Iris Code. IEEE Trans Pattern Anal Mach Intell. 31, 964–973.
  • Blum, A, Langley, P. Selection of relevant features and examples in machine learning. Artif Intell. 1997, 10: 245–271.
  • Wegner, J K, Frohlich, H, Zell, A. Feature Selection for Descriptor Based Classification Models. 1. Theory and GA-SEC Algorithm. J. Chem. Inf. Comput. Sci. 2004, 44: 921-930.
  • Attarchi, S, Faez, K, Asghari, A. A Fast and Accurate Iris Recognition Method Using the Complex Inversion Map and 2DPCA. Int. Conf. Computer and Information Science, 2008, 179-184.
  • Chen, W S, Chuan, C A, Shih, S W, Chang, S H. IRIS RECOGNITION USING 2D-LDA + 2D-PCA. IEEE Int. Conf. Acousts. Speech Signal Process. 2009, 869-872.
  • Chu, C T, Chen, C H. High Performance Iris Recognition Based on LDA and LPCC. In: Proc. IEEE Int. Conf. Tools with Artif. Intell. 2005, 5 pages.
  • Dorairaj, V, Schmid, N A, Fahmy, G. Performance Evaluation of Iris Based Recognition System Implementing PCA and ICA Encoding Techniques. In: Proc. SPIE Conf. Biometric Technology for Human Identification. 2005, 5779: 8 pages.
  • Go, H J, Kwak, K C, Kwon, M J, Chun, M G. Iris Pattern Recognition Using Fuzzy LDA Method. In: International Conference on Knowledge-Based Intelligent Information and Engineering Systems, Springer Lecture Notes in Computer Science, 2005, 3682: 364-370.
  • Ranjzad, H, Ebrahimi, A, Sadigh, H E. Improving Feature Vectors for Iris Recognition through Design and Implementation of New Filter bank and locally compound using of PCA and ICA, Int. Symp. Applied Sciences on Biomedical and Communication Technologies, 2008, 1-5.
  • Son, B, Won, H, Kee, G, Lee, Y. Discriminant Iris Feature and Support Vector Machines For Iris Recognition. IEEE Int. Conf. Image Process. 2004, 2: 865-868.
  • Zhiping, Z, Maomao, H, Ziwen, S. An Iris Recognition Method Based on 2DWPCA and Neural Network. Chinese Control and Decision Conference, 2009, 2357-2360.
  • Konak, A, Coit, D W, Smith, A E. Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering and System Safety. 2006, 91: 992–1007.
  • Bhanu, B, Lin, Y. Genetic algorithm based feature selection for target detection in SAR images. Image and Vision Computing. 2003, 21: 591–608.
  • Pereira, M B, Veiga, A C P. Application of Genetic Algorithms to Improve the Reliability of an Iris Recognition System. IEEE Workshop on Machine Learning for Signal Processing. 2005, 159-164.
  • Roy, K, Bhattacharya, P, Suen C Y. Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs. Engineering Applications of Artificial Intelligence. 2011, 24: 458-475.
  • CASIA iris image database, http://www.cbsr.ia.ac.cn/.
  • UBIRIS datasetobtainedfromdepartmentofcomputerscience,UniversityofBeira Interior, Portugal. /http://iris.di.ubi.pt/S.
  • Tsai, C C, Lin, H Y, Taur, J S, Tao, C W. A New Matching Approach for Local Feature Based Iris Recognition Systems. IEEE Conf. Industrial Electronics and Applications, 2010, 387-392.
  • Masek, L. Recognition of human iris patterns for biometric identification. Bachelor thesis, University of Western Australia. 2003, http://www.csse.uwa.edu.au/˜pk/studentprojects/libor/.
  • Ma, L, Tan, T, Wang, Y, Zhang, D. Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Process. 2004, 13: 739–750.
  • Rathgeb, C, Uhl, A,Wild, P. on Combining Selective Best Bits of Iris-Codes. In: European workshop on Biometrics and ID Management, Springer Lecture Notes in Computer Science, 2011, 6583: 227-237.
  • Tajbakhsh, N, Araabi, B N, Soltanian-Zadeh, H. Noisy iris verification: a modified version of local intensity variation method. In: International Conference on Advances in Biometrics , Springer Lecture Notes in Computer Science, 2009, 5558: 1150–1159.
  • Pinheiro, C F M, Costa, M G F, Filho, C F F C. Applying a Novelty Filter as a Matching Criterion to Iris Recognition. Int. Cong. Image and Signal Process. 2010, 5: 2414-2418.
  • Chen, W S, Huang, R H, Hsieh, L. Iris Recognition Using 3D Co-occurrence Matrix. In: International Conference on Advances in Biometrics, Springer Lecture Notes in Computer Science, 2009, 5558: 1122-1131.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp