FVFSNet: Frequency-Spatial Coupling Network for Finger Vein Authentication

Abstract

Finger vein biometrics is becoming an important source of human authentication due to its advantages in terms of liveness detection, high security, and user convenience. Although there exist a lot of deep learning-based methods for finger vein authentication, they only extract features from finger vein images in the spatial domain and may lose some important information that is present in other domains, such as the frequency domain. Motivated by this conjecture and the remarkable performance of image feature extraction in the frequency domain, this work explores a method capable of extracting finger vein features in both the spatial and frequency domains. Therefore, the features extracted from different domains can complement each other. In addition, we propose a novel frequency-spatial coupling network (FVFSNet) for finger vein authentication. FVFSNet is mainly composed of three parts: (1) the frequency domain processing module (FDPM), (2) the spatial domain processing module (SDPM), and (3) the frequency-spatial coupling module (FSCM). The FDPM is used to extract the finger vein features present in the frequency domain, which is mainly composed of the frequency-spatial domain transformation and the frequency domain convolution layer. The SDPM is used to extract the finger vein features present in the spatial domain, which is mainly composed of convolution layers with an efficient design. The FSCM is used to couple the features extracted from the FDPM and SDPM, which is mainly composed of the channel and spatial attention mechanisms. To validate our conjecture and the performances of FVFSNet, extensive experiments are conducted on nine commonly used publicly available finger vein datasets. Experimental results show that the frequency domain constitutional neural network has a surprising effect on finger vein authentication, and the proposed FVFSNet achieves the state-of-the-art performance with the advantages of lightweight and low computational cost.