Adaptive dictionary learning based on local configuration pattern for face recognition
Abstract Sparse representation based on classification and collaborative representation based classification with regularized least square has been successfully used in face recognition. The over-completed dictionary is crucial for the approaches based on sparse representation or collaborative repre...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-05-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13634-020-00676-5 |
| _version_ | 1818979638289891328 |
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| author | Dongmei Wei Tao Chen Shuwei Li Dongmei Jiang Yuefeng Zhao Tianping Li |
| author_facet | Dongmei Wei Tao Chen Shuwei Li Dongmei Jiang Yuefeng Zhao Tianping Li |
| author_sort | Dongmei Wei |
| collection | DOAJ |
| description | Abstract Sparse representation based on classification and collaborative representation based classification with regularized least square has been successfully used in face recognition. The over-completed dictionary is crucial for the approaches based on sparse representation or collaborative representation because it directly determines recognition accuracy and recognition time. In this paper, we proposed an algorithm of adaptive dictionary learning according to the inputting testing image. First, nearest neighbors of the testing image are labeled in local configuration pattern (LCP) subspace employing statistical similarity and configuration similarity defined in this paper. Then the face images labeled as nearest neighbors are used as atoms to build the adaptive representation dictionary, which means all atoms of this dictionary are nearest neighbors and they are more similar to the testing image in structure. Finally, the testing image is collaboratively represented and classified class by class with this proposed adaptive over-completed compact dictionary. Nearest neighbors are labeled by local binary pattern and microscopic feature in the very low dimension LCP subspace, so the labeling is very fast. The number of nearest neighbors is changeable for the different testing samples and is much less than that of all training samples generally, which significantly reduces the computational cost. In addition, atoms of this proposed dictionary are these high dimension face image vectors but not lower dimension LCP feature vectors, which ensures not only that the information included in face image is not lost but also that the atoms are more similar to the testing image in structure, which greatly increases the recognition accuracy. We also use the Fisher ratio to assess the robustness of this proposed dictionary. The extensive experiments on representative face databases with variations of lighting, expression, pose, and occlusion demonstrate that the proposed approach is superior both in recognition time and in accuracy. |
| first_indexed | 2024-12-20T17:02:43Z |
| format | Article |
| id | doaj.art-2fc517076f96497abaaa92fc4a42a5a8 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-12-20T17:02:43Z |
| publishDate | 2020-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-2fc517076f96497abaaa92fc4a42a5a82022-12-21T19:32:26ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802020-05-012020111210.1186/s13634-020-00676-5Adaptive dictionary learning based on local configuration pattern for face recognitionDongmei Wei0Tao Chen1Shuwei Li2Dongmei Jiang3Yuefeng Zhao4Tianping Li5Shandong Provincial Engineering and Technical Center of Light Manipulations & Shandong Provincial Key Laboratory of Optics and Photonic Device, School of Physics and Electronics, Shandong Normal UniversityShandong Provincial Engineering and Technical Center of Light Manipulations & Shandong Provincial Key Laboratory of Optics and Photonic Device, School of Physics and Electronics, Shandong Normal UniversityShandong Provincial Engineering and Technical Center of Light Manipulations & Shandong Provincial Key Laboratory of Optics and Photonic Device, School of Physics and Electronics, Shandong Normal UniversitySchool of Electronic Information, Qingdao UniversityShandong Provincial Engineering and Technical Center of Light Manipulations & Shandong Provincial Key Laboratory of Optics and Photonic Device, School of Physics and Electronics, Shandong Normal UniversityShandong Provincial Engineering and Technical Center of Light Manipulations & Shandong Provincial Key Laboratory of Optics and Photonic Device, School of Physics and Electronics, Shandong Normal UniversityAbstract Sparse representation based on classification and collaborative representation based classification with regularized least square has been successfully used in face recognition. The over-completed dictionary is crucial for the approaches based on sparse representation or collaborative representation because it directly determines recognition accuracy and recognition time. In this paper, we proposed an algorithm of adaptive dictionary learning according to the inputting testing image. First, nearest neighbors of the testing image are labeled in local configuration pattern (LCP) subspace employing statistical similarity and configuration similarity defined in this paper. Then the face images labeled as nearest neighbors are used as atoms to build the adaptive representation dictionary, which means all atoms of this dictionary are nearest neighbors and they are more similar to the testing image in structure. Finally, the testing image is collaboratively represented and classified class by class with this proposed adaptive over-completed compact dictionary. Nearest neighbors are labeled by local binary pattern and microscopic feature in the very low dimension LCP subspace, so the labeling is very fast. The number of nearest neighbors is changeable for the different testing samples and is much less than that of all training samples generally, which significantly reduces the computational cost. In addition, atoms of this proposed dictionary are these high dimension face image vectors but not lower dimension LCP feature vectors, which ensures not only that the information included in face image is not lost but also that the atoms are more similar to the testing image in structure, which greatly increases the recognition accuracy. We also use the Fisher ratio to assess the robustness of this proposed dictionary. The extensive experiments on representative face databases with variations of lighting, expression, pose, and occlusion demonstrate that the proposed approach is superior both in recognition time and in accuracy.http://link.springer.com/article/10.1186/s13634-020-00676-5Collaborative representation classificationNearest neighborsLocal configuration pattern (LCP)Statistical similarityConfiguration similarity |
| spellingShingle | Dongmei Wei Tao Chen Shuwei Li Dongmei Jiang Yuefeng Zhao Tianping Li Adaptive dictionary learning based on local configuration pattern for face recognition EURASIP Journal on Advances in Signal Processing Collaborative representation classification Nearest neighbors Local configuration pattern (LCP) Statistical similarity Configuration similarity |
| title | Adaptive dictionary learning based on local configuration pattern for face recognition |
| title_full | Adaptive dictionary learning based on local configuration pattern for face recognition |
| title_fullStr | Adaptive dictionary learning based on local configuration pattern for face recognition |
| title_full_unstemmed | Adaptive dictionary learning based on local configuration pattern for face recognition |
| title_short | Adaptive dictionary learning based on local configuration pattern for face recognition |
| title_sort | adaptive dictionary learning based on local configuration pattern for face recognition |
| topic | Collaborative representation classification Nearest neighbors Local configuration pattern (LCP) Statistical similarity Configuration similarity |
| url | http://link.springer.com/article/10.1186/s13634-020-00676-5 |
| work_keys_str_mv | AT dongmeiwei adaptivedictionarylearningbasedonlocalconfigurationpatternforfacerecognition AT taochen adaptivedictionarylearningbasedonlocalconfigurationpatternforfacerecognition AT shuweili adaptivedictionarylearningbasedonlocalconfigurationpatternforfacerecognition AT dongmeijiang adaptivedictionarylearningbasedonlocalconfigurationpatternforfacerecognition AT yuefengzhao adaptivedictionarylearningbasedonlocalconfigurationpatternforfacerecognition AT tianpingli adaptivedictionarylearningbasedonlocalconfigurationpatternforfacerecognition |