Finger vein recognition based on (2D)² PCA and metric learning.
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%.
School of Computer Science and Technology, Shandong University, Jinan 250101, China.
Gongping
G
Yang
Xiaoming
X
Xi
Yilong
Y
Yin
D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D016208:Databases, Factual; D005260:Female; D005385:Fingers / Q000098:blood supply*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008297:Male; D008954:Models, Biological; D016571:Neural Networks, Computer; D025341:Principal Component Analysis