Abstract
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%.
Highlights
Finger vein recognition is a promising biometric recognition technology which verifies identities through finger vein patterns
This paper proposes a new finger vein recognition method based on (2D)2 PCA and metric learning
We address the class imbalance problem by using SMOTE oversampling before the classifier is trained
Summary
Finger vein recognition is a promising biometric recognition technology which verifies identities through finger vein patterns. The finger vein images are matched based on the extracted features. There are two challenges for finger vein recognition: (1) how to efficiently extract distinguishing features and (2) how to design a strong classifier with high recognition rate and fast recognition speed to make the system more practical in real-world applications. To overcome these two challenges, in this paper we apply (2D) PCA to extract the features from finger vein images. It reduces the size of corresponding covariance matrix and obtains the feature projection matrix with less time.
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