Iris Recognition : A Machine Learning Approach

Kaushik Roy

ISBN 10: 3639082591 / ISBN 13: 9783639082593

Published by VDM Verlag Dr. Müller, 2008

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - The increasing demand for enhanced security in the daily life has directed the improvement of the reliable and intelligent personal identification system based on biometrics. Iris recognition has been regarded as one of the most reliable biometrics technologies in recent years. In this book, an iris recognition scheme is presented as a biometrically based technology for person identification using Multi-Class Support Vector Machines (SVM). The Multi-Objectives Genetic Algorithms (MOGA) is used to select the most significant features in order to increase the matching accuracy. The traditional SVM is modified into an asymmetrical SVM to treat the cases of False Accept and False Reject differently and also to control the unbalanced data of a specific class with respect to the other classes. In order to improve the generalization performance of SVM, the optimal values of SVM parameters are selected. The experimental results indicate that the proposed iris recognition scheme can be applied to a wide range of security-related application areas with an encouraging recognition rate.

Seller Inventory # 9783639082593

Bibliographic Details

• Title: Iris Recognition : A Machine Learning Approach
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2008
• Language: English
• ISBN 10: 3639082591
• ISBN 13: 9783639082593
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 8D millimeters 220 millimeters width by 150 millimeters height by 8 millimeters depth
• Weight: 224 grams

About this title

Synopsis

The increasing demand for enhanced security in the daily life has directed the improvement of the reliable and intelligent personal identification system based on biometrics. Iris recognition has been regarded as one of the most reliable biometrics technologies in recent years. In this book, an iris recognition scheme is presented as a biometrically based technology for person identification using Multi-Class Support Vector Machines (SVM). The Multi- Objectives Genetic Algorithms (MOGA) is used to select the most significant features in order to increase the matching accuracy. The traditional SVM is modified into an asymmetrical SVM to treat the cases of False Accept and False Reject differently and also to control the unbalanced data of a specific class with respect to the other classes. In order to improve the generalization performance of SVM, the optimal values of SVM parameters are selected. The experimental results indicate that the proposed iris recognition scheme can be applied to a wide range of security-related application areas with an encouraging recognition rate.

About the Author

Kaushik Roy received B.Sc. and M.Sc. in Computer Science from Rajshahi University, Bangladesh and Concordia University, Canada respectively. He is currently pursuing his PhD at Concordia University, Canada. His research interests include Pattern Recognition, Biometrics, Image Processing and Computer Vision. He has over 30 publications.