An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Guide to Biometrics
A robust eyelash detection based on iris focus assessment
Pattern Recognition Letters
Iris recognition based on score level fusion by using SVM
Pattern Recognition Letters
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Improving Features Subset Selection Using Genetic Algorithms for Iris Recognition
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Optimal features subset selection and classification for iris recognition
Journal on Image and Video Processing - Regular
Image Averaging for Improved Iris Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Iris recognition using signal-level fusion of frames from video
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
An Anticorrelation Kernel for Subsystem Training in Multiple Classifier Systems
The Journal of Machine Learning Research
Iris recognition using genetic algorithms and asymmetrical SVMs
Machine Graphics & Vision International Journal
Engineering Applications of Artificial Intelligence
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
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We propose an iris recognition system for the identification of persons using support vector machines. Canny’s edge detection and the Hough transform are used to find the iris/pupil boundary and a simple thresholding method is employed for eyelash detection. The Gabor wavelet technique is deployed in order to extract the deterministic features in the transformed iris of a person in the form of template. The extracted iris features are fed into a support vector machine (SVM) for classification. Our results indicate that the performance of SVM as a classifier is far better than the performance of a classifier based on the artificial neural network.