Evidential Value of Automated Latent Fingerprint Comparison: An Empirical Approach

Abstract

Abstract-Latent fingerprints are routinely recovered from crime scenes and are compared with available databases of known fingerprints for identifying criminals. However, current procedures to compare latent fingerprints to large databases of full (rolled or plain) fingerprints are prone to errors. This suggests caution in making conclusions about a suspect's identity based on a latent fingerprint comparison. A number of attempts have thus been made to measure the utility of a fingerprint comparison in making a correct accept/reject decision or its evidential value. These approaches, however, either do not represent the stateof-the-art in fingerprint matching due to unrealistic modeling assumptions or they lack simple interpretation. We argue that the posterior probability of two fingerprints belonging to different fingers given their match score, referred to as the Non-match probability (NMP), effectively captures any implicating evidence of the comparison. NMP is computed using state-of-the-art matchers and is easy to interpret. To incorporate the effect of image quality, number of minutiae, and size of the latent on NMP value, we compute the NMP vs. match score plots separately for image pairs (latent and full fingerprints) with different characteristics. Given the paucity of latent fingerprint databases in public domain, we simulate latent fingerprints using two full fingerprint databases (NIST SD-14 and Michigan State Police) by cropping regions of three different sizes. We appropriately validate this simulation using four latent databases (NIST SD-27 and three proprietary latent databases) and two state-of-the-art fingerprint matchers to compute their respective match scores. We also describe the way a latent fingerprint examiner would use the proposed framework to compute the evidential value of a latent-full print pair comparison in practice.

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