Abstract
In this paper, a novel method called tangent space discriminant analysis is proposed for dimensionality reduction and feature extraction. Differing from the recently proposed manifold learning methods completely operating on raw feature space, TSDA completely uses the local tangent space to represent the local within-class geometry and local between-class geometry. Assume that the face images of different people reside on different intrinsically low-dimensional sub-manifolds, TSDA is developed to preserve the locality of each sub-manifold and simultaneously maximize the local separability of different sub-manifolds by using local tangent space alignment. Experimental results show that TSDA achieves higher recognition rates than a few the state-of-the-art techniques.
Original language | English |
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Title of host publication | 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings |
Pages | 3793-3796 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 1 Dec 2010 |
Event | 2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong Duration: 26 Sept 2010 → 29 Sept 2010 |
Conference
Conference | 2010 17th IEEE International Conference on Image Processing, ICIP 2010 |
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Country/Territory | Hong Kong |
City | Hong Kong |
Period | 26/09/10 → 29/09/10 |
Keywords
- Face recogntion
- Feature extraction
- Local tangent space alignment
- Manifold learning
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing