Finger vein extraction using gradient normalization and principal curvature

Joon Hwan Choi; Wonseok Song; Taejeong Kim; Seung-Rae Lee; Hee Chan Kim

doi:10.1117/12.810458

2 February 2009 Finger vein extraction using gradient normalization and principal curvature

Joon Hwan Choi, Wonseok Song, Taejeong Kim, Seung-Rae Lee, Hee Chan Kim

Proceedings Volume 7251, Image Processing: Machine Vision Applications II; 725111 (2009) https://doi.org/10.1117/12.810458
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

Abstract

Finger vein authentication is a personal identification technology using finger vein images acquired by infrared imaging. It is one of the newest technologies in biometrics. Its main advantage over other biometrics is the low risk of forgery or theft, due to the fact that finger veins are not normally visible to others. Extracting finger vein patterns from infrared images is the most difficult part in finger vein authentication. Uneven illumination, varying tissues and bones, and changes in the physical conditions and the blood flow make the thickness and brightness of the same vein different in each acquisition. Accordingly, extracting finger veins at their accurate positions regardless of their thickness and brightness is necessary for accurate personal identification. For this purpose, we propose a new finger vein extraction method which is composed of gradient normalization, principal curvature calculation, and binarization. As local brightness variation has little effect on the curvature and as gradient normalization makes the curvature fairly uniform at vein pixels, our method effectively extracts finger vein patterns regardless of the vein thickness or brightness. In our experiment, the proposed method showed notable improvement as compared with the existing methods.

Citation Download Citation

Joon Hwan Choi, Wonseok Song, Taejeong Kim, Seung-Rae Lee, and Hee Chan Kim "Finger vein extraction using gradient normalization and principal curvature", Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 725111 (2 February 2009); https://doi.org/10.1117/12.810458

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