# A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes
## 1 Introduction  
Fingerprints are one important piece of evidence used in law enforcement to identify criminals. To obtain the identity information of the criminal suspect, firstly, fingerprints collected at crime scenes usually require a fingerprint expert to manually mark the fingerprint features. Then, the collected fingerprint features are compared with the fingerprints recorded in the public security system database. Finally, the identity information of the suspect is obtained through fingerprint information [1; 2; 3]. However, manual fingerprint identification is time-consuming and heavily dependent on experience, so it is difficult for manual fingerprints to complete the fingerprint comparison for large fingerprint databases. With the introduction of an automatic fingerprint identification system (AFIS) [4], the scope of manual investigation is narrowed and the workload of fingerprint experts is greatly reduced. However, negative factors such as background noise, contaminants (blood, etc. ), and human sabotage may affect the integrity of the collected fingerprint images [5; 6; 7]. Insufficient effective area of fingerprint images will reduce the extractability of fingerprint features, which will directly lead to a decrease in fingerprint identification accuracy. Researchers have tried to increase the effective area of fingerprints by using image inpainting methods. However, the existing fingerprint reconstruction methods can only recover small damages, but cannot recover serious and huge damaged areas. The fingerprints collected at the crime scene are severely damaged, which makes it impossible to effectively increase the effective area of the fingerprint by using the fingerprint recovery method. Although the damaged area of the image can be reconstructed using a GANnetwork, the new fingerprint image generated by the GAN network is not exactly the same as the ground truth. Therefore, extracting features from limited fingerprint areas is an important direction for partial fingerprint identification. The minutiae-based method is a typical method in fingerprint image identification [8; 9; 10]. [PERSON] et al. [10] proposed a minutiae-based method to extract useful information from fingerprints. Firstly, the collected fingerprint image is segmented [11; 12] to obtain the fingerprint foreground area. Secondly, enhancement operations on the segmented images are performed [13; 14]. Then, the refined skeleton map of the ridge line of the fingerprint image is extracted via morphological transformation and the minutiae features on the thinned ridges are obtained via neighborhood analysis. Finally, the identification result is obtained by comparing the minutiae points [15]. Due to the limited effective area of the partial fingerprint and the complex background, the minutiae extracted using the traditional algorithm are not enough for fingerprint identification. To improve identification accuracy, many papers combine fingerprint minutiae points with other features such as ridge structure [16; 17], orientation field information [18], sweat pores [19], etc. The feature-combination method improves the identification accuracy of low-quality fingerprints to a certain extent. However, the combined feature method is not suitable for partial fingerprints due to the incomplete and distorted ridgeline structure of the fingerprints collected on-site. At present, deep learning has made a lot of achievements in the field of image identification due to its powerful feature extraction ability [20; 21]. Compared with traditional identification methods, deep learning-based methods makes better use of the effective information of each fingerprint image to realize fingerprint identification. [PERSON] et al. [22] proposed a fingerprint identification algorithm based on support vector machines (SVMs). This method integrates fingerprint minutiae and region information and studies the effect of fingerprint region on the performance of partial fingerprint identification.