Artificial Neural Networks: Theory and Applications
Artificial Neural Networks: Theory and Applications
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modular thinking: evolving modular neural networks for visual guidance of agents
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A novel method to extract features for iris recognition system
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
UBIRIS: a noisy iris image database
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
Improving iris recognition accuracy via cascaded classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A new accurate technique for iris boundary detection
WSEAS Transactions on Computers
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In this paper, we propose a neural network based iris recognition approach by analyzing iris patterns. The iris recognition system consists of iris localization, feature extraction and classification of the iris images. Hough transforms were used for localizing the iris region; Cartesian to polar coordinate transform was used for transforming the ring shaped iris image to the rectangular shape. Then, histogram equalization was applied to the iris image for making the shapes in image more distinctive. Average absolute deviation (AAD) algorithm was used for feature extraction in this approach. In matching process, Multi-Layered Perceptron (MLP) and Modular Neural Networks (MNN) are applied to the iris feature vector for classifying the iris images. In fact, this research is focused on measuring the performance of MNN in iris recognition system compared with Multi-Layered Perceptron (MLP) neural network. The gray-level iris images, experimented in this work, were obtained from Institute of Automation Chinese Academy of Science (CASIA) iris images database and Departments of Informatics University of Beira Interior (UBIRIS) iris images database. Experimental results are given in the last stage of this paper.