Kernelized Fast Algorithm for Regularized Hull-Based Face Recognition With Image Sets
Great success in face recognition has been achieved in recent years; however, complex variations and low-resolution images remain a challenge for unconstrained face recognition. Face recognition in video or image sets, which is known as image-set-based face recognition (ISFR), is one feasible soluti...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8368237/ |
| _version_ | 1828891783289896960 |
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| author | Hengliang Tan Ying Gao |
| author_facet | Hengliang Tan Ying Gao |
| author_sort | Hengliang Tan |
| collection | DOAJ |
| description | Great success in face recognition has been achieved in recent years; however, complex variations and low-resolution images remain a challenge for unconstrained face recognition. Face recognition in video or image sets, which is known as image-set-based face recognition (ISFR), is one feasible solution to address this problem. Regularized nearest points (RNP) is an effective hull-based ISFR method which uses linear space as the input. However, nonlinearity usually exists when the input data contain complex structures, such as illumination and pose variations. Hence, we propose to map the input data to a higher dimensional feature space by using kernel functions, and we develop the kernel extension of the efficient iterative solver to find the regularized nearest points between two sets in higher dimensional feature space. We also exploit this kernel efficient iterative solver to improve the kernel convex hull image-set-based collaborative representation and classification method. The proposed kernelized fast algorithm improves the face recognition ability of RNP and significantly accelerates the kernel version hull-based ISFR methods. Experiments are performed on three benchmark face recognition video data sets. The experimental results illustrate the effectiveness of our proposed methods. |
| first_indexed | 2024-12-13T13:24:35Z |
| format | Article |
| id | doaj.art-d5061a2094c14ff7a7415752d25822f4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:24:35Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d5061a2094c14ff7a7415752d25822f42022-12-21T23:44:20ZengIEEEIEEE Access2169-35362018-01-016363953640710.1109/ACCESS.2018.28418558368237Kernelized Fast Algorithm for Regularized Hull-Based Face Recognition With Image SetsHengliang Tan0https://orcid.org/0000-0003-2167-156XYing Gao1https://orcid.org/0000-0002-2390-530XSchool of Computer Science and Educational Software, Guangzhou University, Guangzhou, ChinaSchool of Computer Science and Educational Software, Guangzhou University, Guangzhou, ChinaGreat success in face recognition has been achieved in recent years; however, complex variations and low-resolution images remain a challenge for unconstrained face recognition. Face recognition in video or image sets, which is known as image-set-based face recognition (ISFR), is one feasible solution to address this problem. Regularized nearest points (RNP) is an effective hull-based ISFR method which uses linear space as the input. However, nonlinearity usually exists when the input data contain complex structures, such as illumination and pose variations. Hence, we propose to map the input data to a higher dimensional feature space by using kernel functions, and we develop the kernel extension of the efficient iterative solver to find the regularized nearest points between two sets in higher dimensional feature space. We also exploit this kernel efficient iterative solver to improve the kernel convex hull image-set-based collaborative representation and classification method. The proposed kernelized fast algorithm improves the face recognition ability of RNP and significantly accelerates the kernel version hull-based ISFR methods. Experiments are performed on three benchmark face recognition video data sets. The experimental results illustrate the effectiveness of our proposed methods.https://ieeexplore.ieee.org/document/8368237/Face recognitionvideo surveillanceimage set classificationoptimization methodsKernel trickefficient iterative solver |
| spellingShingle | Hengliang Tan Ying Gao Kernelized Fast Algorithm for Regularized Hull-Based Face Recognition With Image Sets IEEE Access Face recognition video surveillance image set classification optimization methods Kernel trick efficient iterative solver |
| title | Kernelized Fast Algorithm for Regularized Hull-Based Face Recognition With Image Sets |
| title_full | Kernelized Fast Algorithm for Regularized Hull-Based Face Recognition With Image Sets |
| title_fullStr | Kernelized Fast Algorithm for Regularized Hull-Based Face Recognition With Image Sets |
| title_full_unstemmed | Kernelized Fast Algorithm for Regularized Hull-Based Face Recognition With Image Sets |
| title_short | Kernelized Fast Algorithm for Regularized Hull-Based Face Recognition With Image Sets |
| title_sort | kernelized fast algorithm for regularized hull based face recognition with image sets |
| topic | Face recognition video surveillance image set classification optimization methods Kernel trick efficient iterative solver |
| url | https://ieeexplore.ieee.org/document/8368237/ |
| work_keys_str_mv | AT hengliangtan kernelizedfastalgorithmforregularizedhullbasedfacerecognitionwithimagesets AT yinggao kernelizedfastalgorithmforregularizedhullbasedfacerecognitionwithimagesets |