Abstract
A feature-level fusion approach is proposed for improving the efficiency of palmprint identification. Multiple Gabor filters are employed to extract the phase information on a palmprint image, which is then merged according to a fusion rule to produce a single feature called the Fusion Code. The similarity of two Fusion Codes is measured by their normalized hamming distance. A database containing 7,752 palmprint images from 386 different palms is used to validate the performance of the proposed method. Empirically comparing our previous non-fusion approach and the proposed method, improvement in verification is ensured.
Original language | English |
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Pages (from-to) | 761-767 |
Number of pages | 7 |
Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Volume | 3072 |
Publication status | Published - 1 Dec 2004 |
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science