On matching latent to latent fingerprints

Sankaran, Anush; Dhamecha, Tejas I.; Vatsa, Mayank; Singh, Richa

doi:10.1109/ijcb.2011.6117525

Cited by 50 publications

(32 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that one of the factors determining this prior probability would be the probability that the multiple latent fingerprints belong to the same person. See [43] for an analysis of latent to latent capabilities of available fingerprint matchers.…”

Section: Nmp: the Evidence Of A Fingerprintmentioning

confidence: 99%

Evidential Value of Automated Latent Fingerprint Comparison: An Empirical Approach

Nagar

Choi

Jain

2012

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

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.

show abstract

Section: Nmp: the Evidence Of A Fingerprintmentioning

confidence: 99%

Evidential Value of Automated Latent Fingerprint Comparison: An Empirical Approach

Nagar

Choi

Jain

2012

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

show abstract

“…Hence our proposed matcher takes manually marked minutiae as input and, therefore, it is consistent with existing practice. There have also been some studies on fusion of multiple matchers [21] or multiple latent prints [22].…”

Section: B Latent Fingerprint Matchingmentioning

confidence: 99%

Latent fingerprint matching using descriptor-based hough transform

Paulino

Feng

Jain

2011

2011 International Joint Conference on Biometrics (IJCB)

View full text Add to dashboard Cite

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.

show abstract

“…The IIIT-D latent fingerprint database was introduced by Sankaran et al [19]. It consists of 1046 images of which 1025 contain fingermarks.…”

Section: Iiit-d Latent Fingerprint Databasementioning

confidence: 99%

Identification performance of evidential value estimation for ridge-based biometrics

Kotzerke

Hao

Davis

et al. 2016

EURASIP J. on Info. Security

View full text Add to dashboard Cite

Law enforcement agencies around the world use ridge-based biometrics, especially fingerprints, to fight crime. Fingermarks that are left at a crime scene and identified as potentially having evidential value (EV) in a court of law are recorded for further forensic analysis. Here, we test our evidential value algorithm (EVA) which uses image features trained on forensic expert decisions for 1428 fingermarks to produce an EV score for an image. First, we study the relationship between whether a fingermark is assessed as having EV, either by a human expert or by EVA, and its correct and confident identification by an automatic identification system. In particular, how often does an automatic system achieve identification when the mark is assessed as not having evidential value? We show that when the marks are captured by a mobile phone, correct and confident automatic matching occurs for 257 of the 1428. Of these, 236 were marked as having sufficient EV by experts and 242 by EVA thresholded on equal error rate. Second, we test four relatively challenging ridge-based biometric databases and show that EVA can be successfully applied to give an EV score to all images. Using EV score as an image quality value, we show that in all databases, thresholding on EV improves performance in closed set identification. Our results suggest an EVA application that filters fingermarks meeting a minimum EV score could aid forensic experts at the point of collection, or by flagging difficult latents objectively, or by pre-filtering specimens before submission to an AFIS.

show abstract

On matching latent to latent fingerprints

Cited by 50 publications

References 10 publications

Evidential Value of Automated Latent Fingerprint Comparison: An Empirical Approach

Evidential Value of Automated Latent Fingerprint Comparison: An Empirical Approach

Latent fingerprint matching using descriptor-based hough transform

Identification performance of evidential value estimation for ridge-based biometrics

Contact Info

Product

Resources

About