DocumentCode :
3559948
Title :
Boosted Online Learning for Face Recognition
Author :
Masip, David ; Lapedriza, ?€gata ; Vitri? , Jordi
Volume :
39
Issue :
2
fYear :
2009
fDate :
4/1/2009 12:00:00 AM
Firstpage :
530
Lastpage :
538
Abstract :
Face recognition applications commonly suffer from three main drawbacks: a reduced training set, information lying in high-dimensional subspaces, and the need to incorporate new people to recognize. In the recent literature, the extension of a face classifier in order to include new people in the model has been solved using online feature extraction techniques. The most successful approaches of those are the extensions of the principal component analysis or the linear discriminant analysis. In the current paper, a new online boosting algorithm is introduced: a face recognition method that extends a boosting-based classifier by adding new classes while avoiding the need of retraining the classifier each time a new person joins the system. The classifier is learned using the multitask learning principle where multiple verification tasks are trained together sharing the same feature space. The new classes are added taking advantage of the structure learned previously, being the addition of new classes not computationally demanding. The present proposal has been (experimentally) validated with two different facial data sets by comparing our approach with the current state-of-the-art techniques. The results show that the proposed online boosting algorithm fares better in terms of final accuracy. In addition, the global performance does not decrease drastically even when the number of classes of the base problem is multiplied by eight.
Keywords :
face recognition; feature extraction; image classification; learning (artificial intelligence); principal component analysis; boosted online learning; face classifier; face recognition; linear discriminant analysis; online feature extraction; principal component analysis; reduced training set; verification tasks; Face recognition; incremental learning; multitask learning (MTL); online learning; small sample size problem; Algorithms; Artificial Intelligence; Cluster Analysis; Face; Humans; Pattern Recognition, Automated; Poisson Distribution; Principal Component Analysis;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
Conference_Location :
12/16/2008 12:00:00 AM
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2008.2007497
Filename :
4717267
Link To Document :
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