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Research Article Open Access
Impressions of fingers lifted from crime scenes (latent prints) identification is complex task and is extremely important to law enforcement agencies. Partial fingerprints are usually called latents with small area, nonlinear distortion, and are usually blurred and smudgy. Because of these characteristics and fetures, they have a significantly smaller number of minutiae points (one of the most important features in fingerprint matching) and therefore it can be extremely difficult to automatically match latents to plain or rolled fingerprints that are stored in law enforcement databases. The main aim is to develop a latent matching algorithm that uses only minutiae information under noise. The proposed approach consists of following three modules: (i) align two sets of minutiae by using a descriptor-based Hough Transform; (ii) establish the correspondences between minutiae; and (iii) compute a similarity score under three cases noise, noiseless and by Noise reduction.