Abstract
Face recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This paper discusses the dimensionality reduction (DR) of face images under the framework of SRC. Although one important merit of SRC is that it is insensitive to DR or feature extraction, a well trained projection matrix can lead to higher FR rate at a lower dimensionality. An SRC oriented unsupervised DR algorithm is proposed in this paper and the experimental results on benchmark face databases demonstrated the improvements brought by the proposed DR algorithm over PCA or random projection based DR under the SRC framework.
Original language | English |
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Title of host publication | Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010 |
Pages | 1237-1240 |
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 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition