Abstract
This paper presents a novel supervised manifold learning method called Neighborhood Discriminative Manifold Projection (NDMP) for face recognition in video. By constructing a discriminative eigenspace projection of the high-dimensional face manifold, NDMP seeks to learn an optimal low-dimensional projection by solving a constrained least-squares objective function based on local and global constraints. Local geometry is preserved through the use of intra-class and inter-class neighborhood information while global manifold structure is retained by imposing rotational invariance. The proposed method is comprehensively evaluated on a large video data set. Experimental results and comparisons with classical and state-of-art methods demonstrate the effectiveness of our method.
Original language | English |
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Title of host publication | 2011 International Conference on Pattern Analysis and Intelligence Robotics |
Publisher | IEEE |
Pages | 13-18 |
Number of pages | 6 |
ISBN (Electronic) | 9781612844060 |
DOIs | |
Publication status | Published - 4 Aug 2011 |
Event | 2011 International Conference on Pattern Analysis and Intelligent Robotics - Putrajaya, Malaysia Duration: 28 Jun 2011 → 29 Jun 2011 |
Conference
Conference | 2011 International Conference on Pattern Analysis and Intelligent Robotics |
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Abbreviated title | ICPAIR 2011 |
Country/Territory | Malaysia |
City | Putrajaya |
Period | 28/06/11 → 29/06/11 |
Keywords
- Manifold learning
- pattern recognition
- subspace projection methods
- video-based face recognition
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Human-Computer Interaction