DocumentCode :
2481958
Title :
Directed Random Subspace Method for Face Recognition
Author :
Harandi, Mehrtash T. ; Ahmadabadi, Majid Nili ; Araabi, Babak N. ; Bigdeli, Abbas ; Lovell, Brian C.
Author_Institution :
NICTA, St. Lucia, QLD, Australia
fYear :
2010
fDate :
23-26 Aug. 2010
Firstpage :
2688
Lastpage :
2691
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.
Keywords :
face recognition; learning (artificial intelligence); visual databases; directed random subspace method; ensemble learning; face databases; face recognition; feature discrimination map; probabilistic recall mode; random feature selection; Accuracy; Databases; Face; Face recognition; Frequency division multiplexing; Markov processes; Training data; Face recognition; random subspace method; reinforcement learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
ISSN :
1051-4651
Print_ISBN :
978-1-4244-7542-1
Type :
conf
DOI :
10.1109/ICPR.2010.659
Filename :
5596002
Link To Document :
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