Multispectral iris recognition utilizing hough transform and modified LBP

This paper presents a multispectral iris recognition scheme using Circular Hough Transform (CHT) and a modified Local Binary Pattern (mLBP) feature extraction technique. The CHT is used to localize the iris regions from the multispectral iris images. We also apply the binary thresholding and edge detection techniques in an effort to reduce the effects of over and under segmentation in multispectral iris images in which iris and pupil boundaries are not clearly separable. Furthermore, we apply mLBP in an attempt to elicit the iris feature elements. The mLBP technique combines both the sign and magnitude features for the improvement of iris texture classification performance. The identification and verification performance of the proposed scheme is validated using a multispectral iris dataset of 3120 images.

[1]  Yanggon Kim,et al.  Pupil and Iris Localization for Iris Recognition in Mobile Phones , 2006, Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD'06).

[2]  Libor Masek,et al.  MATLAB Source Code for a Biometric Identification System Based on Iris Patterns , 2003 .

[3]  Arun Ross,et al.  Exploring multispectral iris recognition beyond 900nm , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[4]  Rui Chen,et al.  Liveness detection for iris recognition using multispectral images , 2012, Pattern Recognit. Lett..

[5]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Kaushik Roy,et al.  Iris Recognition Using Fuzzy Level Set and GEFE , 2014 .

[7]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.