# A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes
## 1 Introduction
The results show that with the reduction of the effective area of the fingerprint image, the identification accuracy of partial fingerprints will also decrease. [PERSON] et al. [23] simulated a convolutional neural networks (CNNs)-based fingerprint image identification method and compared it with the traditional minutiae-based identification method. They found that the CNN-based method has fewer iterations and higher identification accuracy in the training process, but the network structure is still not ideal for the identification of partial fingerprints. Some researchers advocate improving the structure of the fingerprint feature extraction network to improve the identification accuracy. For example, [PERSON] et al. [24] adopted different deep convolutional neural networks to learn high-level global features and low-level detail features of fingerprints, which could accurately identify high-resolution (2000 dpi) partial fingerprint images collected on mobile devices. However, this is not ideal for low-resolution fingerprint images. As far as we know, there is no identification method for partial fingerprint images. In the process of fingerprint identification, extracting the features that can accurately describe the fingerprint information from the limited effective area of the partial fingerprint is a key step in identification. In this paper, a novel model named APFI is proposed for the identification of low-quality partial fingerprints. Firstly, a residual network model for partial fingerprint feature extraction is established. The Squeeze and Excitation module (SE module) [25] is added to the network, which is utilized to weight features with cross-channels, focusing on details of partial fingerprints. Next, a specific loss function is designed to train the proposed model to make the features learned by the network more discriminative. The similarity of fingerprint images is defined by the angular distance between fingerprint features. Lastly, to verify the effectiveness of APFI, a home-made mutilated fingerprint dataset is built. 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 contributions of our work can be summarized as follows:For the task of on-site partial fingerprinting, we propose APFI. APFI focuses on the effective area of the fingerprint, which enables it to extract fingerprint features efficiently when the fingerprint image is incomplete. 2. During the training process, a specific loss function is used to train APFI, so that the features learned by the network are more discriminative. At the same time, to accurately calculate the similarity of the two fingerprint images, the angular distance is used to calculate the distance between the features. 3. The SE module is used to adaptively modulate the features, so that the relevant information is retained and the irrelevant information is weakened. Experiments on a NIST-SD4 dataset and a home-made dataset demonstrate the effectiveness and competitiveness of our proposed model.