Fingerprints are the most widely used of all biological characteristics in public safety and forensic identification. However, fingerprint images extracted from the crime scene are incomplete. On the one hand, due to the lack of effective area in partial fingerprint images, the extracted features are insufficient. On the other hand, a broken ridge may lead to a large number of false feature points, which affect the accuracy of fingerprint recognition. Existing fingerprint identification methods are not ideal for partial fingerprint identification. To overcome these problems, this paper proposes an attention-based partial fingerprint identification model named APFI. Firstly, the algorithm utilizes the residual network (ResNet) for feature descriptor extraction, which generates a representation of spatial information on fingerprint expression. Secondly, the channel attention module is inserted into the proposed model to obtain more accurate fingerprint feature information from the residual block. Then, to improve the identification accuracy of partial fingerprints, the angular distance between features is used to calculate the similarity of fingerprints. Finally, the proposed model is trained and validated on a home-made partial fingerprint image dataset. Experiments on the home-made fingerprint datasets and the NIST-SD4 datasets show that the partial fingerprint identification method proposed in this paper has higher identification accuracy than other state-of-the-art methods.

The recovery of mutilated fingerprints plays an important role in improving the accuracy of fingerprint recognition and the speed of identity retrieval, so it is crucial to recover mutilated fingerprints efficiently and accurately. In this paper, we propose a method for the restoration of mutilated fingerprints based on the ridge texture and orientation field. First, the part to be restored is identified via the local quality of the fingerprint, and a mask image is generated. Second, a novel dual-stream fingerprint restoration network named IFSR is designed, which contains two branches, namely an orientation prediction branch guided by the fingerprint orientation field and a detail restoration branch guided by the high-quality fingerprint texture image, through which the damaged region of the mutilated fingerprint is restored. Finally, the method proposed in this paper is validated on a real dataset and an artificially damaged fingerprint dataset. The equal error rate (EER) achieved on the DB1, DB2, and DB4 datasets of FVC2002 is 0.10%, 0.12%, and 0.20%, respectively, while on the DB1, DB2, and DB4 datasets of FVC2004, the EER reaches 1.13%, 2.00%, and 0.27%, respectively. On the artificially corrupted fingerprint dataset, the restoration method achieves a peak signal-to-noise ratio (PSNR) of 16.6735.