Latent Fingerprint Matching: Performance Gain via Feedback from Exemplar Prints

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 b...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2014-12, Vol.36 (12), p.2452-2465
Main Authors: Arora, Sunpreet S., Eryun Liu, Kai Cao, Jain, Anil K.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Biometric Identification - methods
candidate list
Data mining
Databases, Factual
Dermatoglyphics
exemplar feedback
Feature extraction
feature refinement
Feedback
Fingerprint
Fingerprint recognition
Fingerprints
Gain
Humans
Image Processing, Computer-Assisted - methods
latent fingerprint matching
Latent fingerprints
Legal aspects
Matching
NIST
Pattern analysis
Pattern matching
Ridges
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Summary: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.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2014.2330609