Dorsal Hand Vein Recognition Based On Convolutional Neural Networks

In this paper, we proposed a dorsal hand vein recognition method based on Convolutional Neural Network (CNN), compared the recognition rate of different depth CNN models and analyzed the influence of dataset size on dorsal hand vein recognition rate. Firstly, the region of interest (ROI) of dorsal hand vein images was extracted, and contrast limited adaptive histogram equalization (CLAHE) and Gaussian smoothing filter algorithm were used to preprocess the images. Then Reference-CaffeNet AlexNet and VGG depth CNN were trained to extract image feature. Finally, logistic regression was applied for identification. The experimental results on two different size of dataset shown that the depth of network and size of data set size have different degree effect on recognition rate, the dorsal hand vein recognition rate based on VGG-19 reaches 99.7%. In this paper, we also explored the feasibility of ensemble learning on SqueezeNet. The recognition rate declined slightly with 99.52%, but the model size has been decreased sharply.