Signatured Fingermark Recognition Based on Deep Residual Network

Traditional fingerprint recognition methods based on minutiae have shown great success on for high-quality fingerprint images. However, the accuracy rates are significantly reduced for signatured fingermark on the contract. This paper proposes a signatured fingermark recognition method based on deep learning. Firstly, the proposed method uses deep learning combined with domain knowledge to extract the minutiae of fingermark. Secondly, it searches and calibrates the texture region of interest (ROI). Finally, it builds a deep neural network based on residual blocks, and trains the model through Triplet Loss. The proposed method achieved an equal error rate (EER) of 0.0779 on the self-built database, which is far lower than the traditional methods. It also proves that this method can effectively reduce the labor and time costs during minutiae extraction.