Study for lightweight finger vein recognition based on a small sample

Abstract

To address several common problems of finger vein recognition, a lightweight finger vein recognition algorithm by means of a small sample has been proposed in this study. First of all, a Gabor filter is applied to deal with the images for the purpose of that these processed images can simulate a kind of situation of finger vein at low temperature, such that the generalization ability of the algorithm model can be improved as well. By cutting down the amount of convolutional layers and fully connected layers in VGG-19, a lightweight network can be given. Meanwhile, the activation function of some convolutional layers is replaced to protect the network weight that can be updated successfully. After then, a multi-attention mechanism is introduced to the modified network architecture to result in improving the ability of extracting important features. Finally, a strategy based on transfer learning has been used to reduce the training time in the model training phase. Honestly, it is obvious that the proposed finger vein recognition algorithm has a good performance in recognition accuracy, robustness and speed. The experimental results show that the recognition accuracy can arrive at about 98.45%, which has had better performance in comparison with some existing algorithms.