Overlapped Fingerprints Separation Based on Adaptive Orientation Model Fitting

Overlapped fingerprints are commonly encountered in latent fingerprints lifted from crime scenes. Such overlapped fingerprints can hardly be processed by state-of-the-art fingerprint matchers. Several methods have been proposed to separate the overlapped fingerprints. However, these methods neither provide a robust separation results, nor could be generalized to most overlapped fingerprints. In this paper, we propose a novel overlapped fingerprint separation algorithm based on adaptive orientation model fitting. Different from existing methods, our algorithm estimates the initial orientation fields in a more accurate way and then separates the orientation fields for component fingerprints through an iterative correction process. Experimental results on latent overlapped fingerprints database demonstrate the advantage of our algorithm.