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.
This work was supported by National Natural Science Foundation of China (No. 61105019), Natural Science Foundation of Guangdong Province (S2011040002474), Science and Technology Planning Project of Guangdong Province (2009B030803032, 2011B010200023) and the Fundamental Research Funds for the Central Universities, SCUT (2009ZM0070).
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Pan, M., Kang, W. (2011). Palm Vein Recognition Based on Three Local Invariant Feature Extraction Algorithms. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds) Biometric Recognition. CCBR 2011. Lecture Notes in Computer Science, vol 7098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25449-9_15
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DOI: https://doi.org/10.1007/978-3-642-25449-9_15
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