Ensemble-based discriminant learning with boosting for face recognition
文献类型：期刊论文
| 作者 | Lu, JW ; Plataniotis, KN; Venetsanopoulos, AN; Li, SZ
|
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS
 |
| 出版日期 | 2006 |
| 卷号 | 17期号:1页码:166-178 |
| 关键词 | boosting face recognition (FR) linear discriminant analysis machine learning mixture of linear models small-sample-size (SSS) problem strong learner |
| 英文摘要 | In 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | SAMPLE-SIZE PROBLEM ; ALGORITHMS ; KERNEL ; CLASSIFIERS ; REDUCTION ; SUBSPACES |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000235536300015 |
| 公开日期 | 2015-12-24 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/9335]  |
| 专题 | 自动化研究所_09年以前成果 |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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