Iris recognition using independent component analysis

This paper develops a new method for iris recognition based on independent component analysis. The iris recognition consisted of three major components: image preprocessing, feature extraction and classification. A three-step multiscale approach was employed in image preprocessing to realize iris localization, normalization and enhancement. In iris feature extraction, an efficient approach called independent component analysis was used which was statistically independent to establish features for iris region of interest. Under the same experimental conditions, comparisons with the existing methods demonstrate that the proposed method has an emerging performance.

[1]  David Chandler,et al.  Biometric Product Testing Final Report , 2001 .

[2]  Oscar Déniz-Suárez,et al.  Face recognition using independent component analysis and support vector machines , 2001, Pattern Recognit. Lett..

[3]  Jie Wang,et al.  Iris Feature Extraction Based on Wavelet Packet Analysis , 2006, 2006 International Conference on Communications, Circuits and Systems.

[4]  Kwang In Kim,et al.  Face recognition using kernel principal component analysis , 2002, IEEE Signal Processing Letters.

[5]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[6]  R N Vigário,et al.  Extraction of ocular artefacts from EEG using independent component analysis. , 1997, Electroencephalography and clinical neurophysiology.

[7]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[8]  Andrew D. Back,et al.  A First Application of Independent Component Analysis to Extracting Structure from Stock Returns , 1997, Int. J. Neural Syst..

[9]  Bülent Sankur,et al.  Feature selection in the independent component subspace for face recognition , 2004, Pattern Recognit. Lett..

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

[11]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

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

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

[14]  Nathalie Delfosse,et al.  Adaptive blind separation of independent sources: A deflation approach , 1995, Signal Process..

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

[16]  Wageeh W. Boles A security system based on human iris identification using wavelet transform , 1997, Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97.

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

[18]  Te-Won Lee,et al.  Independent Component Analysis , 1998, Springer US.

[19]  Shiro Ikeda,et al.  Independent component analysis for noisy data -- MEG data analysis , 2000, Neural Networks.

[20]  Petteri Pajunen,et al.  Blind source separation using algorithmic information theory , 1998, Neurocomputing.

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

[22]  Erkki Oja,et al.  Independent Component Analysis for Parallel Financial Time Series , 1998, International Conference on Neural Information Processing.

[23]  Jamal Ahmad Dargham,et al.  Iris recognition using self-organizing neural network , 2002, Student Conference on Research and Development.

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

[25]  Baback Moghaddam,et al.  Principal Manifolds and Probabilistic Subspaces for Visual Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..