Abstract
In this paper a novel subspace learning technique is introduced for facial image analysis. The proposed technique takes into account the symmetry nature of facial images. This information is exploited by properly incorporating a symmetry constraint into the objective function of the Two-Dimensional Linear Discriminant Analysis (2DLDA) to determine symmetric projection vectors. The performance of the proposed Symmetric Two-Dimensional Linear Discriminant Analysis was evaluated on real face recognition databases. Experimental results highlight the superiority of the proposed technique in comparison to standard approach.
Original language | English |
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Title of host publication | 2015 IEEE International Conference on Image Processing (ICIP 2015) |
Subtitle of host publication | Proceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 3185-3189 |
Number of pages | 5 |
ISBN (Electronic) | 9781479983391 |
ISBN (Print) | 9781479983407 |
DOIs | |
Publication status | Published - Jan 2016 |
Event | 2015 IEEE International Conference on Image Processing (ICIP) - Quebec City, ON, Canada Duration: 27 Sep 2015 → 30 Sep 2015 |
Conference
Conference | 2015 IEEE International Conference on Image Processing (ICIP) |
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Country | Canada |
City | Quebec City, ON |
Period | 27/09/15 → 30/09/15 |
Keywords
- facial image analysis
- subspace learning
- symmetry constraint
- two-dimensional linear discriminant analysis