Identifying suspects based on impressions of fingers lifted from crime scenes is a custom procedure that is extremely important to forensics and law enforcement agencies. Latents are partial fingerprints that are usually smudgy, with small area and containing large distortion. Latents have a significantly smaller number of minutiae points compared to full fingerprints due to latent characteristics. The fewer minutiae and the noise characteristics of latents make it harder to automatically match latents with their mated full prints, stored in law enforcement databases. Although a number of algorithms for matching full-to-full fingerprints have been published in the literature, they do not perform well on the latent to full fingerprint matching problem. The proposed algorithm uses a robust alignment algorithm (descriptor-based Hough transform) to align fingerprints and measures similarity between fingerprints by considering both minutiae and orientation field information.
