Iris recognition using Gabor filters optimized by the particle swarm technique

In this paper, an efficient feature extraction algorithm based on optimized Gabor filters and a relative variation analysis approach is presented for iris recognition. The Gabor filters are optimized by tuning the parameters with the particle swarm optimization method. Moreover, a sequential filter scheme is developed to determine the number of filters in the optimal Gabor filter bank. In the preprocessing step, the lower part of the iris image is unwrapped and normalized to a rectangular block which is then decomposed by the optimal Gabor filters. After that, a simple encoding method is adopted to generate a compact iris code. Experimental results show that the performance of the proposed method is encouraging and comparable to those of the existing iris recognition systems.

[1]  Ashok A. Ghatol,et al.  Iris recognition: an emerging biometric technology , 2007 .

[2]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Richard P. Wildes,et al.  A system for automated iris recognition , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[4]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[5]  Rishi Gupta,et al.  Iris Recognition System , 2010 .

[6]  David Zhang,et al.  The relative distance of key point based iris recognition , 2007, Pattern Recognit..

[7]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[8]  Dexin Zhang,et al.  Local intensity variation analysis for iris recognition , 2004, Pattern Recognit..

[9]  P Perona,et al.  Preattentive texture discrimination with early vision mechanisms. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[10]  Boualem Boashash,et al.  A human identification technique using images of the iris and wavelet transform , 1998, IEEE Trans. Signal Process..

[11]  Dexin Zhang,et al.  Personal Identification Based on Iris Texture Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[13]  Zehang Sun,et al.  On-road vehicle detection using evolutionary Gabor filter optimization , 2005, IEEE Transactions on Intelligent Transportation Systems.

[14]  J. G. Daugman.  High confidence personal identification by rapid video analysis of iris texture , 1992, Proceedings 1992 International Carnahan Conference on Security Technology: Crime Countermeasures.

[15]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Stefan Fischer,et al.  Face authentication with Gabor information on deformable graphs , 1999, IEEE Trans. Image Process..

[17]  Dexin Zhang,et al.  Efficient iris recognition by characterizing key local variations , 2004, IEEE Transactions on Image Processing.

[18]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[19]  Richard P. Wildes,et al.  A machine-vision system for iris recognition , 2005, Machine Vision and Applications.