Fingerprint Classification Based on Lightweight Neural Networks

Fast and accurate fingerprint classification is very important in large-scale fingerprint identification system. At present, fingerprint classification model has many problems such as complicated operation, lots of parameters, massive data. In this paper, we present a lightweight neural network for automatic extraction features and classification of fingerprint images. Fingerprint Region of Interest (ROI) images is regarded as the input of the network and fused with the shallow feature map to obtain accurate trend information of the shallow middle line. Transfer learning and fingerprint directional field map are combined to pre-train the lightweight network, then the parameters of the network are optimized and experimentally verified. Experimental results show that the fingerprint ROI is integrated into the deep features, which can improve the fingerprint classification effect. The transfer of the lightweight network model can reduce the network requirements for the target domain data and improve the classification performance of small sample fingerprint images.