Abstract
With growing attention to ensemble learning, in recent years various ensemble methods for face recognition have been proposed that show promising results. Among diverse ensemble construction approaches, random subspace method has received considerable attention in face recognition. Although random feature selection in random subspace method improves accuracy in general, it is not free of serious difficulties and drawbacks. In this paper we present a learning scheme to overcome some of the drawbacks of random feature selection in the random subspace method. The proposed learning method derives a feature discrimination map based on a measure of accuracy and uses it in a probabilistic recall mode to construct an ensemble of subspaces. Experiments on different face databases revealed that the proposed method gives superior performance over the well-known benchmarks and state of the art ensemble methods.
Original language | English |
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Title of host publication | Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010 |
Pages | 2688-2691 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 18 Nov 2010 |
Externally published | Yes |
Event | International Conference on Pattern Recognition 2010 - Istanbul, Türkiye Duration: 23 Aug 2010 → 26 Aug 2010 Conference number: 20th https://ieeexplore.ieee.org/xpl/conhome/5595335/proceeding (Proceedings) |
Publication series
Name | Proceedings - International Conference on Pattern Recognition |
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ISSN (Print) | 1051-4651 |
Conference
Conference | International Conference on Pattern Recognition 2010 |
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Abbreviated title | ICPR 2010 |
Country/Territory | Türkiye |
City | Istanbul |
Period | 23/08/10 → 26/08/10 |
Internet address |