Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TPAMI.2014.2330609 | Pattern Analysis and Machine Intelligence, IEEE Transactions |
Keywords | Field | DocType |
digital forensics,feature extraction,fingerprint identification,image matching,automatic matching,baseline matcher,court of law,exemplar prints,features extraction,feedback paradigm,forensic evidence,latent feature refinement,latent fingerprint matching,latent impressions,latent matching accuracy,matching performance,performance gain,plain fingerprints,rolled fingerprints,Fingerprint,candidate list,exemplar feedback,feature refinement,latent fingerprint matching | Computer vision,Background noise,Pattern recognition,Fingerprint recognition,Computer science,Feature extraction,Fingerprint,NIST,Probabilistic latent semantic analysis,Artificial intelligence,Biometrics,Pattern matching | Journal |
Volume | Issue | ISSN |
36 | 12 | 0162-8828 |
Citations | PageRank | References |
7 | 0.48 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sunpreet S. Arora | 1 | 54 | 6.20 |
| Eryun Liu | 2 | 138 | 11.46 |
| Kai Cao | 3 | 207 | 18.68 |
| Anil Jain | 4 | 33507 | 3334.84 |