Abstract
In this paper, we propose a Multi-Manifold Discriminant Analysis (MMDA) method for face feature extraction and face recognition, which is based on graph embedded learning and under the Fisher discirminant analysis framework. In MMDA, the within-class graph and between-class graph are designed to characterize the within-class compactness and the between-class separability, respectively, seeking for the discriminant matrix that simultaneously maximizing the between-class scatter and minimizing the within-class scatter. In addition, the within-class graph can also represent the sub-manifold information and the between-class graph can also represent the multi-manifold information. The proposed MMDA is examined by using the FERET face database, and the experimental results demonstrate that MMDA works well in feature extraction and lead to good recognition performance.
Original language | English |
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Title of host publication | Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010 |
Pages | 527-530 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 18 Nov 2010 |
Event | 2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey Duration: 23 Aug 2010 → 26 Aug 2010 |
Conference
Conference | 2010 20th International Conference on Pattern Recognition, ICPR 2010 |
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Country/Territory | Turkey |
City | Istanbul |
Period | 23/08/10 → 26/08/10 |
Keywords
- Face recongition
- LDA
- Multi-Manifold learning
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition