Finger Vein Recognition Method Based on Self-Calibrated Convolution

Finger vein recognition is a popular biometric technology. It has the characteristics of live detection and non-contact and is widely used in fields with high security requirements. Due to the influence of the environment and fingers' misplacement, the quality of images collected in this situation is poor. Therefore, the recognition accuracy is usually not ideal. To address this problem, this paper proposes a finger vein recognition algorithm based on self-calibrated convolution. First, with ResNet50 as the backbone network, a Coordinate Attention module is added after the input layer. Then, the original convolution module is replaced by the self-calibration convolution module (SCC). Finally, the joint supervision loss function is introduced to train the model. The simulation results show that, the proposed algorithm can effectively reduce the equal error rate (EER) and improve the recognition accuracy.