Virtual samples and sparse representation‐based classification algorithm for face recognition
Due to the environment and equipment are not controllable, the process of face image acquisition is inevitable to be interfered by external factors, and there are usually only a small number of available face images. Insufficient samples are not conducive to face recognition. Therefore, it is a popu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-03-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2018.5096 |
| _version_ | 1797684257344192512 |
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| author | Yali Peng Lingjun Li Shigang Liu Jun Li Han Cao |
| author_facet | Yali Peng Lingjun Li Shigang Liu Jun Li Han Cao |
| author_sort | Yali Peng |
| collection | DOAJ |
| description | Due to the environment and equipment are not controllable, the process of face image acquisition is inevitable to be interfered by external factors, and there are usually only a small number of available face images. Insufficient samples are not conducive to face recognition. Therefore, it is a popular scheme to produce virtual samples based on the available training samples. In this study, the authors first take the symmetry of human face into account, and propose a novel method to generate virtual samples. Then a representation‐based classification method and the score fusion strategy are applied to both original face images and virtual images to perform face recognition. Several sparse representation‐based classification algorithms are compared on ORL, FERET and GT databases. Experimental results show that the authors’ method is effective for improving the face recognition. |
| first_indexed | 2024-03-12T00:28:01Z |
| format | Article |
| id | doaj.art-3db1bb8a668c4f45b04395b910a32ea7 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:28:01Z |
| publishDate | 2019-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-3db1bb8a668c4f45b04395b910a32ea72023-09-15T10:31:50ZengWileyIET Computer Vision1751-96321751-96402019-03-0113217217710.1049/iet-cvi.2018.5096Virtual samples and sparse representation‐based classification algorithm for face recognitionYali Peng0Lingjun Li1Shigang Liu2Jun Li3Han Cao4Key Laboratory of Modern Teaching Technology, Ministry of EducationXi'an710062People's Republic of ChinaKey Laboratory of Modern Teaching Technology, Ministry of EducationXi'an710062People's Republic of ChinaKey Laboratory of Modern Teaching Technology, Ministry of EducationXi'an710062People's Republic of ChinaSchool of Automation, Southeast UniversityNanjing210096People's Republic of ChinaKey Laboratory of Modern Teaching Technology, Ministry of EducationXi'an710062People's Republic of ChinaDue to the environment and equipment are not controllable, the process of face image acquisition is inevitable to be interfered by external factors, and there are usually only a small number of available face images. Insufficient samples are not conducive to face recognition. Therefore, it is a popular scheme to produce virtual samples based on the available training samples. In this study, the authors first take the symmetry of human face into account, and propose a novel method to generate virtual samples. Then a representation‐based classification method and the score fusion strategy are applied to both original face images and virtual images to perform face recognition. Several sparse representation‐based classification algorithms are compared on ORL, FERET and GT databases. Experimental results show that the authors’ method is effective for improving the face recognition.https://doi.org/10.1049/iet-cvi.2018.5096virtual samplessparse representation-based classification algorithmface recognitionface image acquisitionscore fusion strategyORL database |
| spellingShingle | Yali Peng Lingjun Li Shigang Liu Jun Li Han Cao Virtual samples and sparse representation‐based classification algorithm for face recognition IET Computer Vision virtual samples sparse representation-based classification algorithm face recognition face image acquisition score fusion strategy ORL database |
| title | Virtual samples and sparse representation‐based classification algorithm for face recognition |
| title_full | Virtual samples and sparse representation‐based classification algorithm for face recognition |
| title_fullStr | Virtual samples and sparse representation‐based classification algorithm for face recognition |
| title_full_unstemmed | Virtual samples and sparse representation‐based classification algorithm for face recognition |
| title_short | Virtual samples and sparse representation‐based classification algorithm for face recognition |
| title_sort | virtual samples and sparse representation based classification algorithm for face recognition |
| topic | virtual samples sparse representation-based classification algorithm face recognition face image acquisition score fusion strategy ORL database |
| url | https://doi.org/10.1049/iet-cvi.2018.5096 |
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