On Latent Fingerprint Image Quality

Yoon, Soweon; Liu, Eryun; Jain, Anil K.

doi:10.1007/978-3-319-20125-2_7

Cited by 16 publications

(17 citation statements)

References 20 publications

(10 reference statements)

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“…These approaches primarily use features such as the number of minutiae (level-2 feature) and the total area of minutiae region for quality estimation and classify latent prints in {Good, Bad, Ugly} [3] or {VID, } [14] categories. However, these features cannot be used for local quality estimation.…”

Section: Local Quality Assessmentmentioning

confidence: 99%

“…These annotations were studied to develop guidelines, metrics and software tools for assessing fingerprint quality. Yoon et al [14], proposed a semi-automated global quality assessment algorithm for latent fingerprint. The procedure involved analyzing the local ridge pattern continuity, which weighted along with the number of minutiae provided a global quality value.…”

Section: Introductionmentioning

confidence: 99%

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Automated clarity and quality assessment for latent fingerprints

Sankaran

Vatsa

Singh

2013

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

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Clarity of a latent impression is defined as the discernability of fingerprint features while quality is defined as the amount (number) of features contributing towards matching. Automated estimation of clarity and quality at local regions in a latent fingerprint is a research challenge and has received limited attention in the literature. Local clarity and quality helps in better extraction of features and assessing the confidence of matches. The research focuses on (i) developing an automated local clarity estimation algorithm, (ii) developing an automated local quality estimation algorithm based on clarity, and (iii) understanding the correlation between clarity and quality in latent fingerprints. Local clarity assessment is performed using a 2-D linear symmetric structure tensor. The goodness of orientation field is proposed to estimate the local quality of a latent fingerprint. Experiments on the NIST SD-27 database show that incorporating local clarity information in the quality assessment improves the performance of the matching system.

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Section: Local Quality Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated clarity and quality assessment for latent fingerprints

Sankaran

Vatsa

Singh

2013

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

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“…Besides, the exemplar print region from where feedback is being taken may be of poor quality which may not be reliable for feedback. For deciding whether feedback is needed locally, we use the local fingerprint quality metric proposed in [32] called the Ridge Clarity. While this metric was proposed for latent images, we find that it is appropriate for estimating the local quality of exemplar fingerprints (Fig.…”

Section: Local Criterionmentioning

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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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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“…In most cases, the latent fingerprint quality is crucial for latent identification. The research community has developed several approaches and algorithms on fingerprint image quality [11], [12], [14], [15] and latent fingerprint preprocessing [13]. In [14], a latent fingerprint image quality (LFIQ) measurement was proposed.…”

Section: The Software and Tools For Latent Fingerprint Preprocessingmentioning

confidence: 99%

“…The research community has developed several approaches and algorithms on fingerprint image quality [11], [12], [14], [15] and latent fingerprint preprocessing [13]. In [14], a latent fingerprint image quality (LFIQ) measurement was proposed. In [13], Yoon, et al proposed a latent fingerprint enhancement algorithm requiring a manually marked region of interest (ROI) and singular points.…”

Section: The Software and Tools For Latent Fingerprint Preprocessingmentioning

confidence: 99%