Local Pattern Feature Extraction and Ｒecognition Based on Sparse Ｒepresentation
In order to solve the problem that the face image is not rich in features extracted under complex lighting environments，which leads to a low recognition rate，a local pattern feature extraction and recognition algorithm based on sparse representation is proposed. Firstly，the image is divided into sev...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2021-08-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1999 |
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| author | ZHANG Xue-qin LIN Ke-zheng LI Ao |
| author_facet | ZHANG Xue-qin LIN Ke-zheng LI Ao |
| author_sort | ZHANG Xue-qin |
| collection | DOAJ |
| description | In order to solve the problem that the face image is not rich in features extracted under complex lighting environments，which leads to a low recognition rate，a local pattern feature extraction and recognition algorithm based on sparse representation is proposed. Firstly，the image is divided into several sub-images and the Dynamic Threshold Central-symmetric Local Binary Pattern ( DTCLBP) algorithm is used to extract features by thresholding the pixels of each sub-block and encoding the results of comparison with the central pixel values into the Central Symmetric Local Binary Pattern ( CSLBP) ; and then second-order features are extracted from the processed image by the former step using the Central Symmetric Local Derivative Pattern ( CSLDP) ; finally，the sparse representation classification algorithm is used to classify and identify the extracted features. The simulation experiments on Extended Yale B，CMU _PIE and AＲ face databases validate the effectiveness of the DTCLBP-CSLDP-SＲC. |
| format | Article |
| id | doaj-art-daeb4221369144fc9b2750ad468dcd66 |
| institution | DOAJ |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2021-08-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-daeb4221369144fc9b2750ad468dcd662025-08-20T03:02:28ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832021-08-01260410210810.15938/j.jhust.2021.04.014Local Pattern Feature Extraction and Ｒecognition Based on Sparse ＲepresentationZHANG Xue-qin0LIN Ke-zheng1LI Ao2School of Computer Science and Technology，Harbin University of Science and Technology，Harbin 150080，ChinaSchool of Computer Science and Technology，Harbin University of Science and Technology，Harbin 150080，ChinaSchool of Computer Science and Technology，Harbin University of Science and Technology，Harbin 150080，ChinaIn order to solve the problem that the face image is not rich in features extracted under complex lighting environments，which leads to a low recognition rate，a local pattern feature extraction and recognition algorithm based on sparse representation is proposed. Firstly，the image is divided into several sub-images and the Dynamic Threshold Central-symmetric Local Binary Pattern ( DTCLBP) algorithm is used to extract features by thresholding the pixels of each sub-block and encoding the results of comparison with the central pixel values into the Central Symmetric Local Binary Pattern ( CSLBP) ; and then second-order features are extracted from the processed image by the former step using the Central Symmetric Local Derivative Pattern ( CSLDP) ; finally，the sparse representation classification algorithm is used to classify and identify the extracted features. The simulation experiments on Extended Yale B，CMU _PIE and AＲ face databases validate the effectiveness of the DTCLBP-CSLDP-SＲC.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1999central symmetric local binary patterncentral symmetric local derivative patternfeature extractionsparse representation |
| spellingShingle | ZHANG Xue-qin LIN Ke-zheng LI Ao Local Pattern Feature Extraction and Ｒecognition Based on Sparse Ｒepresentation Journal of Harbin University of Science and Technology central symmetric local binary pattern central symmetric local derivative pattern feature extraction sparse representation |
| title | Local Pattern Feature Extraction and Ｒecognition Based on Sparse Ｒepresentation |
| title_full | Local Pattern Feature Extraction and Ｒecognition Based on Sparse Ｒepresentation |
| title_fullStr | Local Pattern Feature Extraction and Ｒecognition Based on Sparse Ｒepresentation |
| title_full_unstemmed | Local Pattern Feature Extraction and Ｒecognition Based on Sparse Ｒepresentation |
| title_short | Local Pattern Feature Extraction and Ｒecognition Based on Sparse Ｒepresentation |
| title_sort | local pattern feature extraction and recognition based on sparse representation |
| topic | central symmetric local binary pattern central symmetric local derivative pattern feature extraction sparse representation |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1999 |
| work_keys_str_mv | AT zhangxueqin localpatternfeatureextractionandrecognitionbasedonsparserepresentation AT linkezheng localpatternfeatureextractionandrecognitionbasedonsparserepresentation AT liao localpatternfeatureextractionandrecognitionbasedonsparserepresentation |