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Journal of Biomedicine and Biotechnology
Volume 2012 (2012), Article ID 324249, 9 pages
Research Article

Finger Vein Recognition Based on (2D)2 PCA and Metric Learning

School of Computer Science and Technology, Shandong University, Jinan 250101, China

Received 22 February 2012; Accepted 19 March 2012

Academic Editor: Sabah Mohammed

Copyright © 2012 Gongping Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. In this paper, (2D)2 PCA is applied to extract features of finger veins, based on which a new recognition method is proposed in conjunction with metric learning. It learns a KNN classifier for each individual, which is different from the traditional methods where a fixed threshold is employed for all individuals. Besides, the SMOTE technology is adopted to solve the class-imbalance problem. Our experiments show that the proposed method is effective by achieving a recognition rate of 99.17%.