Statistical texture analysis-based approach for fake iris detection using support vector machines

Authors:
Xiaofu He;Shujuan An;Pengfei Shi
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
Venue:
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Year:
2007

Citing 11
Cited 1

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Some Defects in Finite-Difference Edge Finders

IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society

Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
High Confidence Visual Recognition of Persons by a Test of Statistical Independence

IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal Identification Based on Iris Texture Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fake iris detection by using purkinje image

ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
A novel iris segmentation method for hand-held capture device

ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Improving iris recognition accuracy via cascaded classifiers

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A real-time focusing algorithm for iris recognition camera

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
How iris recognition works

IEEE Transactions on Circuits and Systems for Video Technology

Efficient Iris Spoof Detection via Boosted Local Binary Patterns

ICB '09 Proceedings of the Third International Conference on Advances in Biometrics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel statistical texture analysis based method for detecting fake iris. Four distinctive features based on gray level co-occurrence matrices (GLCM) and properties of statistical intensity values of image pixels are used. A support vector machine (SVM) is selected to characterize the distribution boundary, for it has good classification performance in high dimensional space. The proposed approach is privacy friendly and does not require additional hardware. The experimental results indicate the new approach to be a very promising technique for making iris recognition systems more robust against fake-iris-based spoofing attempts.