Multi‐view intrinsic low‐rank representation for robust face recognition and clustering
Abstract In the last years, subspace‐based multi‐view face recognition has attracted increasing attention and many related methods have been proposed. However, the most existing methods ignore the specific local structure of different views. This drawback can cause these methods' discriminating...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-12-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12232 |
| _version_ | 1811344420074684416 |
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| author | Zhi‐yang Wang Stanley Ebhohimhen Abhadiomhen Zhi‐feng Liu Xiang‐jun Shen Wen‐yun Gao Shu‐ying Li |
| author_facet | Zhi‐yang Wang Stanley Ebhohimhen Abhadiomhen Zhi‐feng Liu Xiang‐jun Shen Wen‐yun Gao Shu‐ying Li |
| author_sort | Zhi‐yang Wang |
| collection | DOAJ |
| description | Abstract In the last years, subspace‐based multi‐view face recognition has attracted increasing attention and many related methods have been proposed. However, the most existing methods ignore the specific local structure of different views. This drawback can cause these methods' discriminating ability to degrade when many noisy samples exist in data. To tackle this problem, a multi‐view low‐rank representation method is proposed, which exploits both intrinsic relationships and specific local structures of different views simultaneously. It is achieved by hierarchical Bayesian methods that constrain the low‐rank representation of each view so that it matches a linear combination of an intrinsic representation matrix and a specific representation matrix to obtain common and specific characteristics of different views. The intrinsic representation matrix holds the consensus information between views, and the specific representation matrices indicate the diversity among views. Furthermore, the model injects a clustering structure into the low‐rank representation. This approach allows for adaptive adjustment of the clustering structure while pursuing the optimization of the low‐rank representation. Hence, the model can well capture both the relationship between data and the clustering structure explicitly. Extensive experiments on several datasets demonstrated the effectiveness of the proposed method compared to similar state‐of‐the‐art methods in classification and clustering. |
| first_indexed | 2024-04-13T19:48:03Z |
| format | Article |
| id | doaj.art-3c4935c6f5154eacb6c6ceea3268317d |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-13T19:48:03Z |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-3c4935c6f5154eacb6c6ceea3268317d2022-12-22T02:32:40ZengWileyIET Image Processing1751-96591751-96672021-12-0115143573358410.1049/ipr2.12232Multi‐view intrinsic low‐rank representation for robust face recognition and clusteringZhi‐yang Wang0Stanley Ebhohimhen Abhadiomhen1Zhi‐feng Liu2Xiang‐jun Shen3Wen‐yun Gao4Shu‐ying Li5School of Computer Science and Communication Engineering JiangSu University Zhenjiang JiangSu ChinaSchool of Computer Science and Communication Engineering JiangSu University Zhenjiang JiangSu ChinaSchool of Computer Science and Communication Engineering JiangSu University Zhenjiang JiangSu ChinaSchool of Computer Science and Communication Engineering JiangSu University Zhenjiang JiangSu ChinaNanjing LES Information Technology Co., LTD Nanjing JiangSu ChinaSchool of Automation Xi'an University of Posts & Telecommunications Xi'an Shaanxi ChinaAbstract In the last years, subspace‐based multi‐view face recognition has attracted increasing attention and many related methods have been proposed. However, the most existing methods ignore the specific local structure of different views. This drawback can cause these methods' discriminating ability to degrade when many noisy samples exist in data. To tackle this problem, a multi‐view low‐rank representation method is proposed, which exploits both intrinsic relationships and specific local structures of different views simultaneously. It is achieved by hierarchical Bayesian methods that constrain the low‐rank representation of each view so that it matches a linear combination of an intrinsic representation matrix and a specific representation matrix to obtain common and specific characteristics of different views. The intrinsic representation matrix holds the consensus information between views, and the specific representation matrices indicate the diversity among views. Furthermore, the model injects a clustering structure into the low‐rank representation. This approach allows for adaptive adjustment of the clustering structure while pursuing the optimization of the low‐rank representation. Hence, the model can well capture both the relationship between data and the clustering structure explicitly. Extensive experiments on several datasets demonstrated the effectiveness of the proposed method compared to similar state‐of‐the‐art methods in classification and clustering.https://doi.org/10.1049/ipr2.12232Image recognitionComputer vision and image processing techniquesOther topics in statistics |
| spellingShingle | Zhi‐yang Wang Stanley Ebhohimhen Abhadiomhen Zhi‐feng Liu Xiang‐jun Shen Wen‐yun Gao Shu‐ying Li Multi‐view intrinsic low‐rank representation for robust face recognition and clustering IET Image Processing Image recognition Computer vision and image processing techniques Other topics in statistics |
| title | Multi‐view intrinsic low‐rank representation for robust face recognition and clustering |
| title_full | Multi‐view intrinsic low‐rank representation for robust face recognition and clustering |
| title_fullStr | Multi‐view intrinsic low‐rank representation for robust face recognition and clustering |
| title_full_unstemmed | Multi‐view intrinsic low‐rank representation for robust face recognition and clustering |
| title_short | Multi‐view intrinsic low‐rank representation for robust face recognition and clustering |
| title_sort | multi view intrinsic low rank representation for robust face recognition and clustering |
| topic | Image recognition Computer vision and image processing techniques Other topics in statistics |
| url | https://doi.org/10.1049/ipr2.12232 |
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