Effective Iris Recognition for Distant Images Using Log-Gabor Wavelet Based Contourlet Transform Features

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10361)

Abstract

Distant iris recognition has become an active research topic in biometric as well as computer vision, but it is still a very challenging problem. In order to solve it effectively, we propose a novel framework by utilizing Log-Gabor wavelet based Contourlet transform (LGCT) feature descriptor with an effective kernel based extreme learning machine (KELM) classifier. The experiments are conducted on CASIA-v4 which is a typical database of distant iris images. It is demonstrated by the experimental results that our proposed LGCT features are quite effective for distant iris recognition and the highest accuracy can arrive at 95.93% when they are fused with the convolutional neural networks (CNN) and gradient local auto-correlations (GLAC) features together.

Keywords

Iris recognition Distant iris image Log-Gabor wavelet Contourlet transform CNN KELM 
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Notes

Acknowledgment

This work was supported by the Natural Science Foundation of China for Grant 61171138. We also acknowledge the Institute of Automation (Chinese Academy of Science, China) for the contributions of the database employed in this work.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Information Science, School of Mathematical Sciences and LMAMPeking UniversityBeijingChina

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