Palm vein recognition based on three local invariant feature extraction algorithms

Authors:
Mi Pan;Wenxiong Kang
Affiliations:
Department of Control Science and Engineering, Zhejiang University, China and College of Automation Science and Engineering, South China University of Technology, China;College of Automation Science and Engineering, South China University of Technology, China
Venue:
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Year:
2011

Citing 5
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Abstract

In contrast to minutiae features, local invariant features extracted from infrared palm vein have properties of scale, translation and rotation invariance. To determine how they can be best used for palm vein recognition system, this paper conducted a comprehensive comparative study of three local invariant feature extraction algorithms: Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) and Affine-SIFT (ASIFT) for palm vein recognition. First, the images were preprocessed through histogram equalization, then three algorithms were used to extract local features, and finally the results were obtained by comparing the Euclidean distance. Experiments show that they achieve good performances on our own database and PolyU multispectral palmprint database.