A Kernel-Based Probabilistic Collaborative Representation for Face Recognition
A novel classifier for face recognition using an improved probabilistic collaborative representation named IPCR is proposed in this paper. The purpose of this paper is to improve the accuracy of face recognition. The testing sample is assumed to be linearly combined by a part of training samples in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9006801/ |
| _version_ | 1818618594334867456 |
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| author | Jeng-Shyang Pan Xiaopeng Wang Qingxiang Feng Shu-Chuan Chu |
| author_facet | Jeng-Shyang Pan Xiaopeng Wang Qingxiang Feng Shu-Chuan Chu |
| author_sort | Jeng-Shyang Pan |
| collection | DOAJ |
| description | A novel classifier for face recognition using an improved probabilistic collaborative representation named IPCR is proposed in this paper. The purpose of this paper is to improve the accuracy of face recognition. The testing sample is assumed to be linearly combined by a part of training samples in feature space. There is two-phase framework in IPCR. In the first phase, an adjusted parameter of the nearest neighbors of the samples is chosen for classification. In the second phase, a linear combination of the features and the sparse coefficients are used for new patterns. In the process of two-phase framework, the weight matrix is obtained according to the distance between all the training samples and each testing sample, and then it is applied to weight probabilistic collaborative representation coefficients. The kernel trick is implemented for the high-dimensional nonlinear information instead of linear information of data to improve the class separability. The second classifier named KPCR uses a kernel probabilistic collaborative representation for face recognition. Several renowned face databases, e.g., AR, GT, PIE, FERET, and LFW-crop are used for evaluating the performances of the proposed classifiers. The experimental results demonstrate that the proposed classifiers outperform the collaborative representation-based classification (CRC), the probabilistic collaborative representation-based classifier (ProCRC), and the other state-of-the-art classifiers in recognition accuracy. |
| first_indexed | 2024-12-16T17:24:04Z |
| format | Article |
| id | doaj.art-eecc5b3a78db40d688b294e55c246c22 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:24:04Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eecc5b3a78db40d688b294e55c246c222022-12-21T22:23:07ZengIEEEIEEE Access2169-35362020-01-018379463795710.1109/ACCESS.2020.29756229006801A Kernel-Based Probabilistic Collaborative Representation for Face RecognitionJeng-Shyang Pan0https://orcid.org/0000-0002-3128-9025Xiaopeng Wang1https://orcid.org/0000-0002-4330-2151Qingxiang Feng2https://orcid.org/0000-0003-1850-8745Shu-Chuan Chu3https://orcid.org/0000-0003-2117-0618College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaDepartment of Biomedical Engineering, Tulane University, New Orleans, LA, USACollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, ChinaA novel classifier for face recognition using an improved probabilistic collaborative representation named IPCR is proposed in this paper. The purpose of this paper is to improve the accuracy of face recognition. The testing sample is assumed to be linearly combined by a part of training samples in feature space. There is two-phase framework in IPCR. In the first phase, an adjusted parameter of the nearest neighbors of the samples is chosen for classification. In the second phase, a linear combination of the features and the sparse coefficients are used for new patterns. In the process of two-phase framework, the weight matrix is obtained according to the distance between all the training samples and each testing sample, and then it is applied to weight probabilistic collaborative representation coefficients. The kernel trick is implemented for the high-dimensional nonlinear information instead of linear information of data to improve the class separability. The second classifier named KPCR uses a kernel probabilistic collaborative representation for face recognition. Several renowned face databases, e.g., AR, GT, PIE, FERET, and LFW-crop are used for evaluating the performances of the proposed classifiers. The experimental results demonstrate that the proposed classifiers outperform the collaborative representation-based classification (CRC), the probabilistic collaborative representation-based classifier (ProCRC), and the other state-of-the-art classifiers in recognition accuracy.https://ieeexplore.ieee.org/document/9006801/Computer visionface recognitionsparse representationprobabilistic collaborative representation |
| spellingShingle | Jeng-Shyang Pan Xiaopeng Wang Qingxiang Feng Shu-Chuan Chu A Kernel-Based Probabilistic Collaborative Representation for Face Recognition IEEE Access Computer vision face recognition sparse representation probabilistic collaborative representation |
| title | A Kernel-Based Probabilistic Collaborative Representation for Face Recognition |
| title_full | A Kernel-Based Probabilistic Collaborative Representation for Face Recognition |
| title_fullStr | A Kernel-Based Probabilistic Collaborative Representation for Face Recognition |
| title_full_unstemmed | A Kernel-Based Probabilistic Collaborative Representation for Face Recognition |
| title_short | A Kernel-Based Probabilistic Collaborative Representation for Face Recognition |
| title_sort | kernel based probabilistic collaborative representation for face recognition |
| topic | Computer vision face recognition sparse representation probabilistic collaborative representation |
| url | https://ieeexplore.ieee.org/document/9006801/ |
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