| Paper: | MP-P5.6 |
| Session: | Biometrics III: Fingerprints, Iris, Palmprints |
| Time: | Monday, September 17, 14:30 - 17:10 |
| Presentation: |
Poster
|
| Title: |
WAVELET MAXIMA AND MOMENT INVARIANTS BASED IRIS FEATURE EXTRACTION |
| Authors: |
Makram Nabti; Queen’s University Belfast | | |
| | Ahmed Bouridane; Queen’s University Belfast | | |
| Abstract: |
Iris recognition is one of the most reliable personal identification methods and is becoming the most promising technique for high security. In this paper, we propose an efficient method for personal iris identification by investigating iris textures that have a high level of stability and distinctiveness. To improve the efficiency and accuracy of the proposed system, we present a new approach to making a feature vector compact and efficient by using wavelet transform (wavelet maxima components), and moment invariants. The proposed scheme is invariant to translation, rotation, and scale changes. Experimental results have shown that the proposed system could be used for personal identification in an efficient and effective manner. |
