Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Texture detection for segmentation of iris images
SAICSIT '05 Proceedings of the 2005 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
The relative distance of key point based iris recognition
Pattern Recognition
Iris recognition for partially occluded images: methodology and sensitivity analysis
EURASIP Journal on Applied Signal Processing
Biometric scores fusion based on total error rate minimization
Pattern Recognition
Optimal features subset selection and classification for iris recognition
Journal on Image and Video Processing - Regular
Iris recognition using multi-resolution transforms
International Journal of Biometrics
Iris recognition by local extremum points of multiscale Taylor expansion
Pattern Recognition
Iris Matching by Local Extremum Points of Multiscale Taylor Expansion
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
ISA '09 Proceedings of the 3rd International Conference and Workshops on Advances in Information Security and Assurance
Iris segmentation using geodesic active contours
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
A novel method using contourlet to extract features for iris recognition system
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
A novel and efficient method to extract features and vector creation in iris recognition system
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Iris recognition by fusing different representations of multi-scale Taylor expansion
Computer Vision and Image Understanding
Cancellable face biometrics system by combining independent component analysis coefficients
IWCF'10 Proceedings of the 4th international conference on Computational forensics
Flexible-ICA algorithm for a reliable iris recognition
Transactions on large-scale data- and knowledge-centered systems IV
A model based, anatomy based method for synthesizing iris images
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
An accurate and fast iris location method based on the features of human eyes
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
A new feature extraction method using the ICA filters for iris recognition system
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
Shape analysis of stroma for iris recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Biometric key binding: fuzzy vault based on iris images
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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In this paper, we propose a new feature extraction algorithm based on Independent Component Analysis (ICA) for iris recognition. A conventional method based on Gabor wavelets should select the parameters (e.g., spatial location, orientation, and frequency) for fixed bases. We apply ICA to generating optimal basis vectors for the problem of extracting efficient feature vectors which represent iris signals. The basis vectors learned by ICA are localized in both space and frequency like Gabor wavelets. The coefficients of the ICA expansion are used as feature vector. Then, each iris feature vector is encoded into an iris code. Experimental results show that our proposed method has a similar Equal Error Rate (EER) to a conventional method based on Gabor wavelets and two advantages: first, the size of an iris code and the processing time of the feature extraction are significantly reduced; and second, it is possible to estimate the linear transform for feature extraction from the iris signals themselves.