Discriminative multi-scale sparse coding for single-sample face recognition with occlusion
The single sample per person (SSPP) face recognition is a major problem and it is also an important challenge for practical face recognition systems due to the lack of sample data information. To solve SSPP problem, some existing methods have been proposed to overcome the effect of variances to test...
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| Veröffentlicht in: | Pattern recognition Jg. 66; S. 302 - 312 |
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01.06.2017
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| Abstract | The single sample per person (SSPP) face recognition is a major problem and it is also an important challenge for practical face recognition systems due to the lack of sample data information. To solve SSPP problem, some existing methods have been proposed to overcome the effect of variances to test samples in illumination, expression and pose. However, they are not robust when the test samples are with different kinds of occlusions. In this paper, we propose a discriminative multi-scale sparse coding (DMSC) model to address this problem. We model the possible occlusion variations via the learned dictionary from the subjects not of interest. Together with the single training sample per person, most of types of occlusion variations can be effectively tackled. In order to detect and disregard outlier pixels due to occlusion, we develop a multi-scale error measurements strategy, which produces sparse, robust and highly discriminative coding. Extensive experiments on the benchmark databases show that our DMSC is more robust and has higher breakdown point in dealing with the SSPP problem for face recognition with occlusion as compared to the related state-of-the-art methods.
•An intra-class variant dictionary is learned based on PCA.•Propose multi-scale error measurement strategy to improve sparsity and robustness.•An optimization algorithm is proposed to solve multi-scale sparse coding model. |
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| AbstractList | The single sample per person (SSPP) face recognition is a major problem and it is also an important challenge for practical face recognition systems due to the lack of sample data information. To solve SSPP problem, some existing methods have been proposed to overcome the effect of variances to test samples in illumination, expression and pose. However, they are not robust when the test samples are with different kinds of occlusions. In this paper, we propose a discriminative multi-scale sparse coding (DMSC) model to address this problem. We model the possible occlusion variations via the learned dictionary from the subjects not of interest. Together with the single training sample per person, most of types of occlusion variations can be effectively tackled. In order to detect and disregard outlier pixels due to occlusion, we develop a multi-scale error measurements strategy, which produces sparse, robust and highly discriminative coding. Extensive experiments on the benchmark databases show that our DMSC is more robust and has higher breakdown point in dealing with the SSPP problem for face recognition with occlusion as compared to the related state-of-the-art methods.
•An intra-class variant dictionary is learned based on PCA.•Propose multi-scale error measurement strategy to improve sparsity and robustness.•An optimization algorithm is proposed to solve multi-scale sparse coding model. |
| Author | Yu, Yu-Feng Dai, Dao-Qing Ren, Chuan-Xian Huang, Ke-Kun |
| Author_xml | – sequence: 1 givenname: Yu-Feng surname: Yu fullname: Yu, Yu-Feng email: yuyufeng220@163.com organization: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China – sequence: 2 givenname: Dao-Qing surname: Dai fullname: Dai, Dao-Qing email: stsddq@mail.sysu.edu.cn organization: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China – sequence: 3 givenname: Chuan-Xian surname: Ren fullname: Ren, Chuan-Xian email: rchuanx@mail.sysu.edu.cn organization: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China – sequence: 4 givenname: Ke-Kun surname: Huang fullname: Huang, Ke-Kun email: kkcocoon@163.com organization: School of Mathematics, JiaYing University, Meizhou, Guangdong 514015, China |
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| Keywords | Multi-scale Intra-class variant Face recognition Sparse coding Outlier pixels |
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