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.
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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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Ali, L.E., Luo, J., Ma, J. (2017). Effective Iris Recognition for Distant Images Using Log-Gabor Wavelet Based Contourlet Transform Features. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_27
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