Automatic Face Recognition Based on Sparse Representation and Extended Transfer Learning
Sparse representation has exhibited excellent performance in face recognition. However, this method requires some areas for improvement, especially on insufficient face samples. We aim to design a simple and efficient method to improve sparse representation to solve problems with a small sample size...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8543789/ |
| _version_ | 1818641514598760448 |
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| author | Zhi Liu Dongmei Jiang Yujun Li Yankun Cao Mingyu Wang Yong Xu |
| author_facet | Zhi Liu Dongmei Jiang Yujun Li Yankun Cao Mingyu Wang Yong Xu |
| author_sort | Zhi Liu |
| collection | DOAJ |
| description | Sparse representation has exhibited excellent performance in face recognition. However, this method requires some areas for improvement, especially on insufficient face samples. We aim to design a simple and efficient method to improve sparse representation to solve problems with a small sample size. This paper provides two primary contributions that are very effective in small sample face recognition. First, in order to enhance recognition robustness, we designed an intuitive and mathematically controllable transfer learning method of sparse representation by introducing labeled samples. Second, to obtain high recognition accuracy, we developed a weighted fusion scheme to integrate the sparse representation results generated from original and labeled samples. In the ORL dataset, our algorithm’s highest accuracy rate is 95%. In the FERET dataset, our highest classification accuracy rate is 95%. In the more complex LFW dataset, our highest classification accuracy rate has also reached 83.33%. This shows that our experimental results demonstrate that the proposed method can obtain sufficient performance, whereas the weighted fusion scheme can take advantage of sparse representation on the basis of original and labeled samples. This paper will be very useful for identification based on the Internet-of-Medical-Things. |
| first_indexed | 2024-12-16T23:28:23Z |
| format | Article |
| id | doaj.art-af0388f7f35d49f4b1c66a3f7c29acd1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T23:28:23Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-af0388f7f35d49f4b1c66a3f7c29acd12022-12-21T22:11:56ZengIEEEIEEE Access2169-35362019-01-0172387239510.1109/ACCESS.2018.28832888543789Automatic Face Recognition Based on Sparse Representation and Extended Transfer LearningZhi Liu0https://orcid.org/0000-0002-7640-5982Dongmei Jiang1Yujun Li2Yankun Cao3Mingyu Wang4Yong Xu5School of Information Science and Engineering, Shandong University, Qingdao, ChinaSchool of Electronic Information, Qingdao University, Qingdao, ChinaSchool of Information Science and Engineering, Shandong University, Qingdao, ChinaSchool of Information Science and Engineering, Shandong University, Qingdao, ChinaState Key Laboratory of ASIC & Systems, School of Microelectronics, Fudan University, Shanghai, ChinaDepartment of Computer Science and Engineering, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, ChinaSparse representation has exhibited excellent performance in face recognition. However, this method requires some areas for improvement, especially on insufficient face samples. We aim to design a simple and efficient method to improve sparse representation to solve problems with a small sample size. This paper provides two primary contributions that are very effective in small sample face recognition. First, in order to enhance recognition robustness, we designed an intuitive and mathematically controllable transfer learning method of sparse representation by introducing labeled samples. Second, to obtain high recognition accuracy, we developed a weighted fusion scheme to integrate the sparse representation results generated from original and labeled samples. In the ORL dataset, our algorithm’s highest accuracy rate is 95%. In the FERET dataset, our highest classification accuracy rate is 95%. In the more complex LFW dataset, our highest classification accuracy rate has also reached 83.33%. This shows that our experimental results demonstrate that the proposed method can obtain sufficient performance, whereas the weighted fusion scheme can take advantage of sparse representation on the basis of original and labeled samples. This paper will be very useful for identification based on the Internet-of-Medical-Things.https://ieeexplore.ieee.org/document/8543789/Face recognitionimage representationknowledge transferpattern recognition |
| spellingShingle | Zhi Liu Dongmei Jiang Yujun Li Yankun Cao Mingyu Wang Yong Xu Automatic Face Recognition Based on Sparse Representation and Extended Transfer Learning IEEE Access Face recognition image representation knowledge transfer pattern recognition |
| title | Automatic Face Recognition Based on Sparse Representation and Extended Transfer Learning |
| title_full | Automatic Face Recognition Based on Sparse Representation and Extended Transfer Learning |
| title_fullStr | Automatic Face Recognition Based on Sparse Representation and Extended Transfer Learning |
| title_full_unstemmed | Automatic Face Recognition Based on Sparse Representation and Extended Transfer Learning |
| title_short | Automatic Face Recognition Based on Sparse Representation and Extended Transfer Learning |
| title_sort | automatic face recognition based on sparse representation and extended transfer learning |
| topic | Face recognition image representation knowledge transfer pattern recognition |
| url | https://ieeexplore.ieee.org/document/8543789/ |
| work_keys_str_mv | AT zhiliu automaticfacerecognitionbasedonsparserepresentationandextendedtransferlearning AT dongmeijiang automaticfacerecognitionbasedonsparserepresentationandextendedtransferlearning AT yujunli automaticfacerecognitionbasedonsparserepresentationandextendedtransferlearning AT yankuncao automaticfacerecognitionbasedonsparserepresentationandextendedtransferlearning AT mingyuwang automaticfacerecognitionbasedonsparserepresentationandextendedtransferlearning AT yongxu automaticfacerecognitionbasedonsparserepresentationandextendedtransferlearning |