A Novel Method for Finger Vein Recognition

Abstract

Benefiting from CNN’s strong feature expression ability, the finger vein recognition systems using the convolutional neural network (CNN) currently have shown a good performance. However, these systems usually adopt such large networks or complex step-by-step processes that they cannot be applied to the hardware platform with limited computing power and small memory. To address this limitation, this research proposes a finger vein recognition network based on difference image and 3C image for cascade fine-tuning. First, a difference image from the image pair for authentic matching or imposter matching is obtained by difference operation and a 2C image is acquired by regarding this image pair as a two-channel image; furthermore, a 3C image is gained with the channel connection of the difference image and 2C image. Then, the SqueezeNet (this network has been pre-trained on ImageNet) that receives the 3C image as input is fine-tuned and the best fine-tune manner is determined. Finally, a cascade fine-tune framework is designed to integrate the difference images and 3C image. In this paper, the size of SqueezeNet which is cascade fine-tuned on the basis of the pre-training weights is 5.63 MB, and the corresponding equal error rate(EER) acquired on the dataset MMCBNU_6000 and SDUMLA-HMT is 1.889% and 4.906% respectively. The experimental results fully prove that the proposed method achieves not only high recognition accuracy but also the simplification of network.