Name
Abstract | ||
|---|---|---|
Biometric systems examine the uniqueness of an individual based on physical and behavioral characteristics. Among the known traits, fingerprint is the most significant biometric trait due to its ease of use and high accuracy. However, the efficiency of the fingerprint matching technique depends on the feature vector it uses. The ideal feature vector should be invariant to several common transformations, which usually a fingerprint capturing system is subjected to. Current work focuses to achieve such an invariance by extracting the features based on the spatial relationship among minutiae points. We propose a minutiae point based 4-dimensional local feature vector, which simultaneously satisfies six desirable feature vector properties. This feature vector definition helps us to deal with problem of missing and spurious minutiae and thus enables us to design a robust authentication system. We have substantiated the efficacy of the proposed approach with the help of a number of fingerprint instances available in FVC and NIST databases. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11042-017-5444-9 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Biometrics, Image processing, Minutiae triplet, Feature extraction, Fingerprint identification | Feature vector,Pattern recognition,Computer science,Minutiae,Image processing,Fingerprint,Feature extraction,NIST,Invariant (mathematics),Artificial intelligence,Biometrics | Journal |
Volume | Issue | ISSN |
77 | 15 | 1380-7501 |
Citations | PageRank | References |
0 | 0.34 | 25 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tauheed Ahmed | 1 | 0 | 2.03 |
| Monalisa Sarma | 2 | 10 | 5.24 |