Pattern recognition with moment invariants: a comparative study and new results
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Pores and Ridges: Fingerprint Matching Using Level 3 Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint enhancement using STFT analysis
Pattern Recognition
Face detection by neural network trained with Zernike moments
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
Quality-augmented fusion of level-2 and level-3 fingerprint information using DSm theory
International Journal of Approximate Reasoning
K-plet and coupled BFS: a graph based fingerprint representation and matching algorithm
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Object recognition using local characterisation and zernike moments
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
| Hi-index | 0.00 |
Fingerprint friction ridge details are generally described in a hierarchical order at three levels, namely, Level 1 (pattern), Level 2 (minutiae points) and Level 3 (pores and ridge shape). Although high resolution sensors (~ 1000dpi) have become commercially available and have made it possible to reliably extract Level 3 features, most Automated Fingerprint Identification Systems (AFIS) employ only Level 1 and Level 2 features. As a result, increasing the scan resolution does not provide any matching performance improvement. We develop a matcher that utilizes Level 3 features, including pores and ridge contours, in conjunction with level 2 features (minutiae) for matching. The aim is to reduce the error rates, namely FAR (False Acceptance Rate) and FRR (False Rejection Rate) in the existing minutiae based systems. The hierarchical matcher has been tested on two diverse databases in public domain. The obtained results are promising and verify our claim.