Publications:Improving Automated Latent Fingerprint Identification Using Extended Minutia Types
From ISLAB/CAISR
| Title | Improving Automated Latent Fingerprint Identification Using Extended Minutia Types |
|---|---|
| Author | Ram P. Krish and Julian Fierrez and Daniel Ramos and Fernando Alonso-Fernandez and Josef Bigun |
| Year | 2018 |
| PublicationType | Journal Paper |
| Journal | Information Fusion |
| HostPublication | |
| Conference | |
| DOI | http://dx.doi.org/10.1016/j.inffus.2018.10.001 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1254167 |
| Abstract | Latent fingerprints are usually processed with Automated Fingerprint Identification Systems (AFIS) by law enforcement agencies to narrow down possible suspects from a criminal database. AFIS do not commonly use all discriminatory features available in fingerprints but typically use only some types of features automatically extracted by a feature extraction algorithm. In this work, we explore ways to improve rank identification accuracies of AFIS when only a partial latent fingerprint is available. Towards solving this challenge, we propose a method that exploits extended fingerprint features (unusual/rare minutiae) not commonly considered in AFIS. This new method can be combined with any existing minutiae-based matcher. We first compute a similarity score based on least squares between latent and tenprint minutiae points, with rare minutiae features as reference points. Then the similarity score of the reference minutiae-based matcher at hand is modified based on a fitting error from the least square similarity stage. We use a realistic forensic fingerprint casework database in our experiments which contains rare minutiae features obtained from Guardia Civil, the Spanish law enforcement agency. Experiments are conducted using three minutiae-based matchers as a reference, namely: NIST-Bozorth3, VeriFinger-SDK and MCC-SDK. We report significant improvements in the rank identification accuracies when these minutiae matchers are augmented with our proposed algorithm based on rare minutiae features. |
