# A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes
## 2 Related Works
### Partial Fingerprint Identification  
Traditional methods are usually based on minutiae descriptors [29] for fingerprint identification. The corresponding relationship between minutiae is established by considering the similarity of minutiae descriptors, and then fingerprint comparison is performed. To improve the identification performance, many papers combine fingerprint minutiae points with other key information [30; 31] as the feature representation of fingerprints, and then measure the similarity between them. [PERSON] et al. [32] add the ridge feature to the traditional detail feature identification, and the final identification score is the combination of the two identification stages. The identification scheme includes two stages of minutiae identification and ridge feature identification, and the results of the two matching stages are combined to obtain the final identification score. [PERSON] et al. [33] proposed a fingerprint identification field extraction algorithm based on dictionary learning and multi-scale sparse coding. Multi-scale dictionary learning is performed with high-quality fingerprint orientations from various scales, and fingerprint images are decomposed to obtain fingerprint texture images. On texture images, fingerprint patch orientations are iteratively estimated using a multi-scale sparse coding method. [PERSON] et al. [34] combined sweat pore features with fingerprint ridge features and achieved outstanding performance in high-resolution partial fingerprint identification. [PERSON] et al. [35] proposed a robust and fast identification system combining two indexing algorithms. One of the algorithms used triplet minutiae and the other used the orientation domain to retrieve fingerprints, reducing identification time without loss of accuracy.  
For high-resolution fingerprint images, traditional algorithms have achieved excellent results. However, in partial fingerprints, complex background noise and partial ridge will affect the extraction of features. In addition, a large number of false feature points will be generated when the minutiae points of the partial fingerprint image are extracted, which reduces the accuracy of the fingerprint identification.  
At present, fingerprint identification technology based on deep learning has attracted the attention of researchers. [PERSON] et al. [36] proposed a fingerprint classification model based on convolutional neural networks, which achieved good results in various databases and improved the fingerprint identification rate at the same time. [PERSON] et al. [37] used the transfer learning method to design a fingerprint identification model (DCNN) based on deep convolutional neural networks. The experimental results show that the designed DCNN model can be effectively used in fingerprint identification and classification. [PERSON] et al. [38] combined two loss functions to train the designed convolutional neural network. However, this method has certain limitations. On the one hand, the two loss functions will produce different feature distributions, so it may not be a natural choice to combine features. On the other hand, contrastive loss will be affected by data expansion in the training process and the computational cost will increase, which makes it impossible to quickly complete fingerprint identification for a large database. [PERSON] et al. [39] utilize CNN to extract templates (two minutiae ones and one texture one) to realize fuzzy fingerprint identification. The identification scores of each template of the input fingerprint and the reference fingerprint are fused to obtain the final identification result. Although this method has achieved good results, the calculation process is too cumbersome to quickly complete fingerprint identification for large databases.