Finger vein recognition methods based on deep learning usually require a large amount of computing resources, it limits their promotion and popularization on embedded devices. The adoption of lightweight network faces the problem of decreasing accuracy due to the reduction of model parameters. Therefore, this paper proposed a lightweight recognition algorithm based on key features of finger vein and AdaFace loss. In the MicroNet network framework, firstly, this paper proposed FMixconv convolution to replace the deep convolution in the original network, which could obtain multi-scale information of vein features while reducing parameters. Secondly, the method used a lightweight attention module, CA module, to focus on key information of venous characteristics from space and channel. Finally, the algorithm added AdaFace loss into the loss function, through the characteristics of the norm to evaluate image quality, to reduce the impact of image quality degradation on training. The recognition accuracy of the proposed algorithm on SDUMLA-HMT, FV-USM and self-built datasets reached 99.84%, 99.39% and 99.42%, while the number of parameters was only 0.82 M. Experimental results show that the proposed network is ahead of other methods in accuracy and parameter size.

[2023-09-06] Accepted Paper /doc/2023.06.0304/v1

[2024-03-05] Printed Article /doc/2023.06.0304/v2