An Advanced Latent Fingerprint Matching by Using Level Three Features
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 3
Abstract
Latent fingerprint identification is of critical importance in forensic applications. Fingerprint features are generally described at three different levels, namely, Level 1 (ridge flow), Level 2 (minutiae points) and Level 3 (pores, dots and ridge shape, etc.). Current Automated Fingerprint Identification Systems (AFIS) generally rely only on a subset of Level 1 and Level 2 features (minutiae, core & delta) for matching. On the other hand, latent print examiners frequently take advantage of a much richer set of features naturally occurring in fingerprints. It is believed that this difference may be one of the reasons for the superior performance of fingerprint examiners over AFIS, particularly in case of difficult latent matches. Fingerprint features, other than minutiae, core & delta, are also referred to as the extended feature set (EFS). The goal of this study is to i) develop algorithms for encoding and matching extended features, ii) develop fusion algorithms to combine extended features with minutiae information to improve fingerprint matching accuracy, and iii) understand the contributions of various extended features in latent fingerprint matching. Based on extensive experiments, the following findings are observed: i) almost all the extended features lead to some improvement in latent matching accuracy, ii) extended features at higher level are more effective in improving latent matching accuracy than those at lower level, iii) high image resolution (at least 1000 ppi) is necessary but not sufficient for reliably capturing Level 3 features.
Authors and Affiliations
B. Raja Rao, Dr. E. V. Krishna Rao
Keywords
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- EP ID EP23211
- DOI http://doi.org/10.22214/ijraset.2017.3014
- Views 284
- Downloads 6
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