Local SIFT analysis for hand vein pattern verification

Yunxin Wang; Dayong Wang; Tiegen Liu

doi:10.1117/12.837104

18 November 2009 Local SIFT analysis for hand vein pattern verification

Yunxin Wang, Dayong Wang, Tiegen Liu

Proceedings Volume 7512, 2009 International Conference on Optical Instruments and Technology: Optoelectronic Information Security; 751204 (2009) https://doi.org/10.1117/12.837104
Event: International Conference on Optical Instrumentation and Technology, 2009, Shanghai, China

Abstract

The newly emerging hand vein recognition technology has attracted remarkable attention for its uniqueness, noninvasion, friendliness and high reliability. It is unavoidable to produce small location deviation of human hand in the practical application; however, the existing recognition methods are sensitive to the hand shift or rotation. The test sample is matched with a series of registered images after affine transformation including the shift or rotation by most of researches, this affine transform method can remedy the location deviation to some extent, but the limited range for hand shift and rotation brings users much inconvenience and the computational cost also increases greatly. Aiming at this issue, a hand vein recognition algorithm based on local SIFT (Scale Invariant Feature Transform) analysis is developed in this contribution, which has practical significance due to its translation and rotation invariance. First, the hand vein image is preprocessed to remove the background and reduce image noises, and then SIFT features are extracted to describe the gradient information of hand vein. Many one-to-more matching pairs are produced by the common matching method of SIFT features, thus the matching rule is improved by appending a constrained condition to ensure the one-to-one matching, which is achieved by selecting feature point with the nearest distance as the optimal match. Finally the match ratio of features between the registered and test images is calculated as the similarity measurement to verify the personal identification. The experiment results show that FRR (False Rejection Rate) is only 0.93% when FAR (False Acceptance Rate) is 0.002%, and EER (Equal Error Rate) is low to 0.12%, which demonstrate the proposed approach is valid and effective for hand vein authentication.

Citation Download Citation

Yunxin Wang, Dayong Wang, and Tiegen Liu "Local SIFT analysis for hand vein pattern verification", Proc. SPIE 7512, 2009 International Conference on Optical Instruments and Technology: Optoelectronic Information Security, 751204 (18 November 2009); https://doi.org/10.1117/12.837104

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