Neural network based biometric personal identification with fast iris segmentation

This paper presents the iris recognition system for biometric personal identification using neural network. Personal identification consists of localization of the iris region and generation of a data set of iris images followed by iris pattern recognition. In this paper, a fast algorithm is proposed for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement, it is represented by a data set. Using this data set a Neural Network (NN) is used for the classification of iris patterns. The adaptive learning strategy is applied for training of the NN. The results of simulations illustrate the effectiveness of the neural system in personal identification.

[1]  Okhwan Byeon,et al.  Efficient Iris Recognition through Improvement of Feature Vector and Classifier , 2001 .

[2]  Jaihie Kim,et al.  Iris Feature Extraction Using Independent Component Analysis , 2003, AVBPA.

[3]  Siwei Luo,et al.  An efficient iris recognition system , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[4]  F. H. Adler.  Physiology of the eye : clinical application , 1950 .

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

[6]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Tieniu Tan,et al.  Improving iris recognition accuracy via cascaded classifiers , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[8]  John Daugman,et al.  Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns , 2001, International Journal of Computer Vision.

[9]  T. Tan,et al.  Iris Recognition Based on Multichannel Gabor Filtering , 2002 .

[10]  Libor Masek,et al.  Recognition of Human Iris Patterns for Biometric Identification , 2003 .

[11]  Carmen Sanchez-Avila,et al.  Iris-based biometric recognition using dyadic wavelet transform , 2002 .

[12]  Lu Xu,et al.  A Rapid Iris Location Method Based on the Structure of Human Eyes , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[13]  Richard P. Wildes,et al.  Iris recognition: an emerging biometric technology , 1997, Proc. IEEE.

[14]  Lionel Torres,et al.  Person Identification Technique Using Human Iris Recognition , 2002 .

[15]  J. Daugman,et al.  Recognizing iris texture by phase demodulation , 1994 .

[16]  Hiroshi Nakajima,et al.  An Effective Approach for Iris Recognition Using Phase-Based Image Matching , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Mark J. T. Smith,et al.  Iris-Based Personal Authentication Using a Normalized Directional Energy Feature , 2003, AVBPA.

[18]  Stéphane Mallat,et al.  Zero-crossings of a wavelet transform , 1991, IEEE Trans. Inf. Theory.

[19]  Natalia A. Schmid,et al.  Performance evaluation of non-ideal iris based recognition system implementing global ICA encoding , 2005, IEEE International Conference on Image Processing 2005.

[20]  Natalia A. Schmid,et al.  Performance evaluation of iris-based recognition system implementing PCA and ICA encoding techniques , 2005, SPIE Defense + Commercial Sensing.

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

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

[23]  Xi Wang,et al.  Efficient Eye Location for Biomedical Imaging using Two-level Classifier Scheme , 2008 .

[24]  Yong Wang,et al.  Iris recognition using independent component analysis , 2005, 2005 International Conference on Machine Learning and Cybernetics.

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

[26]  Jaihie Kim,et al.  A Novel Method to Extract Features for Iris Recognition System , 2003, AVBPA.

[27]  Josef Kittler,et al.  Audio- and Video-Based Biometric Person Authentication, 5th International Conference, AVBPA 2005, Hilton Rye Town, NY, USA, July 20-22, 2005, Proceedings , 2005, AVBPA.

[28]  John Daugman,et al.  Demodulation by Complex-Valued Wavelets for Stochastic Pattern Recognition , 2003, Int. J. Wavelets Multiresolution Inf. Process..

[29]  F. Scotti.  Computational intelligence techniques for reflections identification in iris biometric images , 2007, 2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.