A novel neural network based approach to latent overlapped fingerprints separation

Overlapped fingerprints are often found in latent fingerprints lifted from crime scenes and in live-scan fingerprint images when the surface of fingerprint sensors contains residues of fingerprints of previous users. Such overlapped fingerprints usually cannot be processed accurately by contemporary commercial fingerprint matchers, which has led many researchers to propose methods designed to separate the overlapped fingerprints. In this paper, we propose a novel latent overlapped fingerprints separation algorithm based on neural networks. Our algorithm works in a block-based fashion. After producing an initial estimation of the orientation fields present in the overlapped fingerprint image, it uses a neural network to separate the mixed orientation fields, which are then post-processed to correct remaining errors and enhanced using the global orientation field enhancement model. Experimental results show that the proposed algorithm outperforms the state-of-the-art algorithm in terms of accuracy on the Tsinghua Overlapped Latent Fingerprint Database (containing real-world overlapped fingerprints obtained by forensic methods), while also showing encouraging results (second only to state-of-the-art) on the Tsinghua Simulated Overlapped Fingerprint Database (containing artificially overlapped fingerprints of a good quality).