Hand Vein Recognition with Single-layer Feature Learning Model

Abstract

Abstract. Performance of feature extraction and representation, sticking point of image recognition task, will directly influence the accuracy of final recognition. The traditional feature extraction algorithm of vein recognition is based on the sufficient prior knowledge of analysis on vein information characteristics, the shortcoming of which reflects in long time consumption spent on tuning parameters and special selection about later classifier to guarantee the final recognition rate as high as possible. The paper makes the attempt to introduce the K-means model, single-layer feature representation architecture, to the vein recognition task with some targeted modification, and adopts the SVM at the link of classifiers design. Finally, the proposed approach is rigorously evaluated on the self-built database and achieves the state-of-the-art RR (Recognition Rate) of 98.34%, which demonstrates the effectiveness of the proposed model.