Abstract
This paper presents a multi-resolution analysis based Independent Component Analysis (ICA) method for automatic palmprint identification. The ICA is well known by its feature representation ability recently, in which the desired representation is the one that minimizes the statistical independence of the components of the representation. Such a representation can capture the essential feature and the structure of the palmprint images. At the same time, the palmprints have a great deal of different features, such as principal lines, wrinkles, ridges, minutiae points and texture, which can be regarded as multi-scale features. Then, it is reasonable for us to integrate the multi-resolution analysis method and ICA to represent the palmprint features. The experiment results show that the integrated method is more efficient than ICA algorithm.
Original language | English |
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Title of host publication | Proceedings of 2004 International Conference on Machine Learning and Cybernetics |
Pages | 3547-3550 |
Number of pages | 4 |
Volume | 6 |
Publication status | Published - 2 Nov 2004 |
Event | Proceedings of 2004 International Conference on Machine Learning and Cybernetics - Shanghai, China Duration: 26 Aug 2004 → 29 Aug 2004 |
Conference
Conference | Proceedings of 2004 International Conference on Machine Learning and Cybernetics |
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Country/Territory | China |
City | Shanghai |
Period | 26/08/04 → 29/08/04 |
Keywords
- Independent Component Analysis
- Multi-resolution Analysis
- Palmprint Identification
ASJC Scopus subject areas
- General Engineering