FV-DMHN: Dual Multi-Head Network for Finger Vein Recognition

Abstract

Deep learning-based finger vein image recognition methods usually suffer from high complexity, insufficient global information extraction, and overfitting. The use of lightweight networks can significantly reduce the accuracy owing to the reduction in model parameters. For this reason, this paper proposes a Dual Multi-Head neural Network for Finger Vein Recognition (FV-DMHN), which combines the Multi-Head Self-Attention (MHSA) mechanism with the Multi-Head Convolutional Network(MHCN) to increase the training efficiency of the network while expanding the CNN horizon. The inverted residual structure is also used in the network to enhance the expressive power of the network. The algorithm achieves recognition accuracies of 99.81%, 99.67%, 99.69%, and 99.83% on three publicly available datasets, FV-USM, SDUMLA-HMT, THU-FVFDT2, and self-built datasets FV-SIPL, respectively, with an average equal-error rate of 0.339%, and the recognition time of a single image is only 2.63 ms. The experimental results show that the algorithm is superior to other methods in terms of accuracy and average equal error rate, at the same time, it not only reduces the number of network parameters and computational complexity but also achieves excellent recognition speed.

Keywords

• Finger vein recognition
• multi-head convolutional network
• inverted residual module
• multi-head self-attention