期刊
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
卷 64, 期 10, 页码 2605-2615出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2015.2427893
关键词
Decision fusion; face recognition; Gabor wavelet; multitask sparse representation; sparse residual
资金
- National Natural Science Foundation of China [61172161]
- National Natural Science Foundation for Distinguished Young Scholars of China [61325007]
- Fundamental Research Funds for Central Universities, Hunan University, Changsha, China
For a human face, the Gabor transform can extract its multiple scale and orientation features that are very useful for the recognition. In this paper, the Gabor-feature-based face recognition is formulated as a multitask sparse representation model, in which the sparse coding of each Gabor feature is regarded as a task. To effectively exploit the complementary yet correlated information among different tasks, a flexible representation algorithm termed multitask adaptive sparse representation (MASR) is proposed. The MASR algorithm not only restricts Gabor features of one test sample to be jointly represented by training atoms from the same class but also promotes the selected atoms for these features to be varied within each class, thus allowing better representation. In addition, to use the local information, we operate the MASR on local regions of Gabor features. Then, by considering the structural characteristics of the face and the effects of the external interferences, a structural-residual weighting strategy is proposed to adaptively fuse the decision of each region. Experiments on various datasets verify the effectiveness of the proposed method in dealing with face occlusion, corruption, small number of training samples, as well as variations of lighting and expression.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据