On latent fingerprint enhancement

Abstract-Latent fingerprints serve as an important source of forensic evidence in a court of law. Automatic matching of latent fingerprints to rolled/plain (exemplar) fingerprints with high accuracy is quite vital for such applications. However, latent impressions are typically of poor quality with complex background noise which makes feature extraction and matching of latents a significantly challenging problem. We propose incorporating top-down information or feedback from an exemplar to refine the features extracted from a latent for improving latent matching accuracy. The refined latent features (e.g. ridge orientation and frequency), after feedback, are used to re-match the latent to the top K candidate exemplars returned by the baseline matcher and resort the candidate list. The contributions of this research include: (i) devising systemic ways to use information in exemplars for latent feature refinement, (ii) developing a feedback paradigm which can be wrapped around any latent matcher for improving its matching performance, and (iii) determining when feedback is actually necessary to improve latent matching accuracy. Experimental results show that integrating the proposed feedback paradigm with a state-of-the-art latent matcher improves its identification accuracy by 0.5-3.5 percent for NIST SD27 and WVU latent databases against a background database of 100k exemplars.

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“…Local Fourier analysis is used for this purpose because it has been shown to be resilient to complex background noise [26], [13].…”

Section: Latent Feature Extraction and Refinementmentioning

confidence: 99%

“…State-of-the-art latent matching systems [11], [12], [13], [14] are based on the classical bottom-up matching strategy [15]. The bottom-up approach basically builds a system from several sub-systems or components.…”

Section: Introductionmentioning

confidence: 99%

Latent Fingerprint Matching: Performance Gain via Feedback from Exemplar Prints

Arora

Liu

Cao

et al. 2014

IEEE Trans. Pattern Anal. Mach. Intell.

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“…10. These inputs are: (i) enhanced latent by manually marked orientation field; (ii) enhanced latent by the estimated orientation field using the proposed algorithm; (iii) enhanced latent by the estimated orientation field using least-squares method [11]; and (iv) latent image with no enhancement.…”

Section: Outputmentioning

confidence: 99%

Latent fingerprint enhancement via robust orientation field estimation

Yoon

Feng

Jain

2011

2011 International Joint Conference on Biometrics (IJCB)

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“…in poor quality latents is very time-consuming and might be only feasible in rare cases. Thus, some studies have concentrated on latent matching using a reduced amount of manual input, such as manually marked region of interest (ROI) and singular points [19], [20]. However, only a small portion of latents can be correctly identified using this approach.…”

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)

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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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On latent fingerprint enhancement

Cited by 55 publications

References 13 publications

Latent Fingerprint Matching: Performance Gain via Feedback from Exemplar Prints

Latent Fingerprint Matching: Performance Gain via Feedback from Exemplar Prints

Latent fingerprint enhancement via robust orientation field estimation

Latent fingerprint matching using descriptor-based hough transform

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