Efficient local representations for three-dimensional palmprint recognition

Bing Yang; Xiaohua Wang; Jinliang Yao; Xin Yang; Wenhua Zhu

doi:10.1117/1.JEI.22.4.043040

20 December 2013 Efficient local representations for three-dimensional palmprint recognition

Bing Yang, Xiaohua Wang, Jinliang Yao, Xin Yang, Wenhua Zhu

Author Affiliations +

Funded by: National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China, School Scientific Research Fund

Journal of Electronic Imaging, Vol. 22, Issue 4, 043040 (December 2013). https://doi.org/10.1117/1.JEI.22.4.043040

Abstract

Palmprints have been broadly used for personal authentication because they are highly accurate and incur low cost. Most previous works have focused on two-dimensional (2-D) palmprint recognition in the past decade. Unfortunately, 2-D palmprint recognition systems lose the shape information when capturing palmprint images. Moreover, such 2-D palmprint images can be easily forged or affected by noise. Hence, three-dimensional (3-D) palmprint recognition has been regarded as a promising way to further improve the performance of palmprint recognition systems. We have developed a simple, but efficient method for 3-D palmprint recognition by using local features. We first utilize shape index representation to describe the geometry of local regions in 3-D palmprint data. Then, we extract local binary pattern and Gabor wavelet features from the shape index image. The two types of complementary features are finally fused at a score level for further improvements. The experimental results on the Hong Kong Polytechnic 3-D palmprint database, which contains 8000 samples from 400 palms, illustrate the effectiveness of the proposed method.

Citation Download Citation

Bing Yang, Xiaohua Wang, Jinliang Yao, Xin Yang, and Wenhua Zhu "Efficient local representations for three-dimensional palmprint recognition," Journal of Electronic Imaging 22(4), 043040 (20 December 2013). https://doi.org/10.1117/1.JEI.22.4.043040

Published: 20 December 2013

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