Deep learning‐based approach to latent overlapped fingerprints mask segmentation

Overlapped fingerprints can be potentially present in several civil applications and criminal investigations. Segmentation of overlapped fingerprints is a required step in the process of fingerprint separation and subsequent verification. Overlapped fingerprint segmentation is performed manually (and the resulting manually drawn masks are a required additional input) in all of the overlapped latent fingerprints separation approaches in the literature, which make them only semi‐automatic. This study proposes a novel overlapped fingerprint mask segmentation approach, thereby filling that gap in the development of fully automated fingerprint separation solutions. The proposed method uses convolutional neural networks to classify image blocks into three classes – background, single region, and overlapped region. The proposed approach shows satisfactory performance on three different datasets and opens the door for full automation of fingerprint separation algorithms, which is a very promising research area.