Ensemble Deep Learning Based Single Finger-Vein Recognition

Finger-vein biometrics has been extensively investigated for personal verification. Single sample per person (SSPP) finger-vein recognition is one of the open issues in finger-vein recognition. In this paper, a deep ensemble learning method is proposed for SSPP finger-vein recognition. To the best of our knowledge, this is the first work proposed for finger-vein identification with single finger-vein sample per person. First, we generate different feature maps from an input image, based on which multiple independent deep learning based classifiers are proposed for finger-vein identification. Second, a shared learning scheme is investigated among classifiers to improve their feature representation captivity. Third, an approach is proposed to adjust learning speed of weak classifiers, so that all classifiers achieve best performance at the same time. Finally, a deep ensemble learning method are proposed by combing all weak classifiers. The experimental results on two public finger-vein databases show that our approach outperforms its all classifiers and existing approaches, and achieves the state-of-the-art recognition results.