Finger vein recognition based on bilinear fusion of multiscale features

Abstract

Finger veins are widely used in various fields due to their high safety. Existing finger vein recognition methods have some shortcomings, such as low recognition accuracy and large model size. To address these shortcomings, a multi-scale feature bilinear fusion network (MSFBF-Net) was designed. First, the network model extracts the global features and local detail features of the finger veins and performs linear fusion to obtain second-order features with richer information. Then, the mixed depthwise separable convolution replaces the ordinary convolution, which greatly reduces the computational complexity of the network model. Finally, a multiple attention mechanism (MAM) suitable for finger veins was designed, which can simultaneously extract the channel, spatial, directional, and positional information. The experimental results show that the method is very effective, and the accuracy of the two public finger vein databases is 99.90% and 99.82%, respectively.