Palm Vein Recognition Based on Competitive Code, LBP and DCA Fusion Strategy

Abstract

Information fusion mainly includes feature level fusion, matching-score level fusion and decision level fusion. However, the feature level fusion is considered to be a more effective fusion method because of the more biometric data than the matching fraction fusion and the decision level fusion. Feature level fusion is the extraction of feature information from the source image and the multiple features are analyzed, processed and integrated to get a single fusion image feature. In this paper, the palm vein features are extracted with Competition code and local binary pattern (LBP), respectively, to obtain two different palm vein features. Then two features are fused using discriminant correlation analysis (DCA). DCA is a feature level fusion technology, which associates class associations to the correlation analysis of feature sets. DCA implements effective feature fusion by maximizing the pairwise correlation between the two feature sets, and eliminating inter class correlation and restricting the correlation within the class. The Competition code uses the directional characteristics of the image to extract the palm vein features, while the LBP is an operator used to describe the local texture features of the image. The two features are complementary. Using DCA to combine the two characteristics of the palm vein can achieve a good classification effect. This paper uses the multispectral near-infrared palm vein image database of Hong Kong Polytech University for testing. Compared with the single palm vein Competition code feature or LBP feature, the DCA combines the two characteristics of the palm vein, which not only shortens the classification time in some degree, but also improves the recognition rate to 99.8% in the case of training samples of 9 and test samples of 3.