Finger Vein Recognition Algorithm Based on Lightweight Deep Convolutional Neural Network

Abstract

Even though the deep neural networks have strong feature representation capability and high recognition accuracy in finger vein recognition, the deep models are computationally intensive and poor in timeliness. To address these issues, this article proposes a lightweight algorithm for finger vein image recognition and matching. The proposed algorithm uses a lightweight convolutional model in the backbone network and employs a triplet loss function to train the model, which not only improves the matching accuracy, but also satisfies the real-time matching requirements. In addition, the Mini-region of interest (RoI) and finger vein pattern feature extraction also effectively solve the problems of large amounts of calculation and background noise. Moreover, the present model recognizes new categories based on the feature vector space constructed by the finger vein recognition system, so that new categories can be recognized without retraining the model. The results show that the finger vein recognition and matching algorithm proposed in this article achieves 99.3% and 99.6% in recognition accuracy and 14.2 and 16.5 ms in matching time for the dataset Shandong University Machine Learning and Applications Laboratory-Homologous Multimodal Biometric Traits (SDUMLA-HMT) and Peking University Finger Vein Dataset (PKU-FVD), respectively. These metrics show that our approach is time-saving and more effective than previous algorithms. Compared with the state-of-the-art finger vein recognition algorithm, the proposed algorithm improves 1.45% in recognition accuracy while saving 45.7% in recognition time.