Abstract

The increase in twin births has created a requirement for biometric systems to accurately determine the identity of a person who has an identical twin. The discriminability of some of the identical twin biometric traits, such as fingerprints, iris, and palmprints, is supported by anatomy and the formation process of the biometric characteristic, which state they are different even in identical twins due to a number of random factors during the gestation period. For the first time, we collected multiple biometric traits (fingerprint, face, and iris) of 66 families of twins, and we performed unimodal and multimodal matching experiments to assess the ability of biometric systems in distinguishing identical twins. Our experiments show that unimodal finger biometric systems can distinguish two different persons who are not identical twins better than they can distinguish identical twins; this difference is much larger in the face biometric system and it is not significant in the iris biometric system. Multimodal biometric systems that combine different units of the same biometric modality (e.g. multiple fingerprints or left and right irises * ) show the best performance among all the unimodal and multimodal biometric systems, achieving an almost perfect separation between genuine and impostor distributions.

Abstract-Identifying suspects based on impressions of fingers lifted from crime scenes (latent prints) is a routine 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. Due to these characteristics, latents have a significantly smaller number of minutiae points compared to full (rolled or plain) fingerprints. The small number of minutiae and the noise characteristic of latents make it extremely difficult to automatically match latents to their mated full prints that are 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 matching problem. Further, they often rely on features that are not easy to extract from poor quality latents. In this paper, we propose a new fingerprint matching algorithm which is especially designed for matching latents. 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. To be consistent with the common practice in latent matching (i.e. only minutiae are marked by latent examiners), the orientation field is reconstructed from minutiae. Since the proposed algorithm relies only on manually marked minutiae, it can be easily used in the law enforcement applications. Experimental results on two different latent databases (NIST SD27 and WVU latent databases) show that the proposed algorithm outperforms two well optimized commercial fingerprint matchers. Further, a fusion of the proposed algorithm and commercial fingerprint matchers leads to improved matching accuracy.

No abstract

Fingerprints have been widely used as a biometric trait for person recognition. Due to the wide acceptance and deployment of fingerprint matching systems, there is a steady increase in the size of fingerprint databases in law enforcement and national ID agencies. Thus, it is of great interest to develop methods that, for a given query fingerprint (rolled or latent), can efficiently filter out a large portion of the reference or background database based on a coarse matching (or indexing) strategy. In this work, we propose an indexing technique, primarily for latents, that combines multiple level 1 and level 2 features to filter out a large portion of the background database while maintaining the latent matching accuracy. Our approach consists of combining minutiae, singular points, orientation field and frequency information. Experimental results carried out on 258 latents in NIST SD27 against a large background database (267K rolled prints) show that the proposed approach outperforms state-of-the-art fingerprint indexing techniques reported in the literature. At a penetration rate of 20%, our approach can reach a hit rate of 90.3%, with a five-fold reduction in the latent search (indexing + matching) time, while maintaining the latent matching accuracy.