Filtering large fingerprint database for latent matching

Feng, Jianjiang; Jain, Anil K.

doi:10.1109/icpr.2008.4761851

Cited by 31 publications

(26 citation statements)

References 6 publications

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“…Feng and Jain [6] proposed a multi-stage filtering scheme whose first stage depends on the fingerprint pattern type, followed by the use of singular points and orientation field. The features are manually marked for the latents, and automatically extracted in the rolled prints.…”

Section: Related Workmentioning

confidence: 99%

Latent fingerprint indexing: Fusion of level 1 and level 2 features

Paulino

Liu

Cao

et al. 2013

2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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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.

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Section: Related Workmentioning

confidence: 99%

Latent fingerprint indexing: Fusion of level 1 and level 2 features

Paulino

Liu

Cao

et al. 2013

2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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“…To reach that end, we first establish the general solution space for (5) such that data fidelity can be governed in the known region Ω when estimating β S .…”

Section: Problem Formulationmentioning

confidence: 99%

“…As suggested by the NIST report Concept of [4] published in 2007, automated search capabilities will assist latent experts by reducing the size of candidate lists they need to examine through elimination of the more obvious "nuisance" non-matches. Recently, Feng and Jain [5] proposes a multi-staged filtering scheme to scan the database in search of the potential candidates for large-scale latent matching. However, the filtering algorithms rely on finding singular points in a partial fingerprint segment.…”

mentioning

confidence: 99%

Global Ridge Orientation Modeling for Partial Fingerprint Identification

Wang

2011

IEEE Trans. Pattern Anal. Mach. Intell.

105

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Abstract-Identifying incomplete or partial fingerprints from a large fingerprint database remains a difficult challenge today. Existing studies on partial fingerprints focus on one-to-one matching using local ridge details. In this paper, we investigate the problem of retrieving candidate lists for matching partial fingerprints by exploiting global topological features. Specifically, we propose an analytical approach for reconstructing the global topology representation from a partial fingerprint. Firstly, we present an inverse orientation model for describing the reconstruction problem. Then, we provide a general expression for all valid solutions to the inverse model. This allows us to preserve data fidelity in the existing segments while exploring missing structures in the unknown parts. We have further developed algorithms for estimating the missing orientation structures based on some a priori knowledge of ridge topology features. Our statistical experiments show that our proposed model-based approach can effectively reduce the number of candidates for pair-wised fingerprint matching, and thus significantly improve the system retrieval performance for partial fingerprint identification.

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“…Improved latent matching accuracy has been reported by using extended features, which are manually marked for latents [15], [16], [17], [18]. However, marking extended features (orientation field, ridge skeleton, etc.)…”

Section: B Latent Fingerprint Matchingmentioning

confidence: 99%

“…[11], [8], [12], [18], [6]). Feng and Zhou [31] evaluated the performance of local descriptors associated with fingerprint matching in four categories of fingerprints: good quality, poor quality, small common region, and large plastic distortion.…”

Section: A Feature Extractionmentioning

confidence: 99%

Latent fingerprint matching using descriptor-based hough transform

Paulino

Feng

Jain

2011

2011 International Joint Conference on Biometrics (IJCB)

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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.

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Filtering large fingerprint database for latent matching

Cited by 31 publications

References 6 publications

Latent fingerprint indexing: Fusion of level 1 and level 2 features

Latent fingerprint indexing: Fusion of level 1 and level 2 features

Global Ridge Orientation Modeling for Partial Fingerprint Identification

Latent fingerprint matching using descriptor-based hough transform

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