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Ensemble-based discriminant learning with boosting for face recognition
Lu, JW; Plataniotis, KN; Venetsanopoulos, AN; Li, SZ
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS
2006
Volume17Issue:1Pages:166-178
SubtypeArticle
AbstractIn this paper, we propose a novel ensemble-based approach to boost performance of traditional Linear Discriminant Analysis (LDA)-based methods used in face recognition. The ensemble-based approach is based on the recently emerged technique known as "boosting:' However, it is generally believed that boosting-like learning rules are not suited to a strong and stable learner such as LDA. To break the limitation, a novel weakness analysis theory is developed here. The theory attempts to boost a strong learner by increasing the diversity between the classifiers created by the learner, at the expense of decreasing their margins 9 so as to achieve a tradeoff suggested by recent boosting studies for a low generalization error. In addition, a novel distribution accounting for the pairwise class discriminant information is introduced for effective interaction between the booster and the LDA-based learner. The integration of all these methodologies proposed here leads to the novel ensemble-based discriminant learning approach, capable of taking advantage of both the boosting and LDA techniques. Promising experimental results obtained on various difficult face recognition scenarios demonstrate the effectiveness of the proposed approach. We believe that this work is especially beneficial in extending the boosting framework to accommodate general (strong/weak) learners.
KeywordBoosting Face Recognition (Fr) Linear Discriminant Analysis Machine Learning Mixture Of Linear Models Small-sample-size (Sss) Problem Strong Learner
WOS HeadingsScience & Technology ; Technology
WOS KeywordSAMPLE-SIZE PROBLEM ; ALGORITHMS ; KERNEL ; CLASSIFIERS ; REDUCTION ; SUBSPACES
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000235536300015
Citation statistics
Cited Times:115[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9335
Collection09年以前成果
Affiliation1.Univ Toronto, Edward S Rogers Sr Dept Elect & Comp Engn, Toronto, ON M5S 3G4, Canada
2.Chinese Acad Sci, Ctr Biometr & Secur Res, Inst Automat, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Lu, JW,Plataniotis, KN,Venetsanopoulos, AN,et al. Ensemble-based discriminant learning with boosting for face recognition[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS,2006,17(1):166-178.
APA Lu, JW,Plataniotis, KN,Venetsanopoulos, AN,&Li, SZ.(2006).Ensemble-based discriminant learning with boosting for face recognition.IEEE TRANSACTIONS ON NEURAL NETWORKS,17(1),166-178.
MLA Lu, JW,et al."Ensemble-based discriminant learning with boosting for face recognition".IEEE TRANSACTIONS ON NEURAL NETWORKS 17.1(2006):166-178.
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