Finger vein recognition based on Deep Convolutional Neural Networks

In the process of a finger vein image acquisition, finger vein images are susceptible to external factors like finger posture and light source conditions, which will result in poor recognition accuracy. Therefore, a finger vein recognition method based on improved convolution neural net work is proposed to improve the accuracy and robustness of the image recognition. Firstly, the collected finger vein image is preprocessed by image segmentation, finger root key point location and image extraction in the region of interest (ROI). Secondly, according to the application context of finger vein recognition, the convolution neural network structure is adjusted appropriately, and the output of convolution layer is standardized in batches. The optimized neural network is used to automatically extract, classify and identify the features of the preprocessed images. A large number of experiments were performed on public finger print data sets of Shandong University. The optimal recognition rates are 90% respectively. The experiments verify the effectiveness of this method.