Single-Sample Face Recognition Based on Feature Expansion
Face recognition (FR) with a single sample per person (SSPP) is one of the most challenging problems in computer vision. In this scenario, it is difficult to predict facial variation such as pose, illumination, and disguise due to the lack of enough training samples. Therefore, the development of th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8681088/ |
| _version_ | 1818617949208969216 |
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| author | Rui Min Shengping Xu Zongyong Cui |
| author_facet | Rui Min Shengping Xu Zongyong Cui |
| author_sort | Rui Min |
| collection | DOAJ |
| description | Face recognition (FR) with a single sample per person (SSPP) is one of the most challenging problems in computer vision. In this scenario, it is difficult to predict facial variation such as pose, illumination, and disguise due to the lack of enough training samples. Therefore, the development of the FR system with only a small number of training samples is hindered. To address this problem, this paper proposes a scheme combined transfer learning and sample expansion in feature space. First, it uses transfer learning by training a deep convolutional neural network on a common multi-sample face dataset and then applies the well-trained model to a target data set. Second, it proposes a sample expansion method in feature space called k class feature transfer (KCFT) to enrich intra-class variation information for a single-sample face feature. Compared with other expanding sample methods in the image domain, this method of expanding the samples in the feature domain is novel and easy to implement. Third, it trains a softmax classifier with expanded face features. The experimental results on ORL, FERET, and LFW face databases demonstrate the effectiveness and robustness of the proposed method for various facial variations. |
| first_indexed | 2024-12-16T17:13:49Z |
| format | Article |
| id | doaj.art-fd56c58f55464b8f8779aeefeca0ea9a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:13:49Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fd56c58f55464b8f8779aeefeca0ea9a2022-12-21T22:23:21ZengIEEEIEEE Access2169-35362019-01-017452194522910.1109/ACCESS.2019.29090398681088Single-Sample Face Recognition Based on Feature ExpansionRui Min0Shengping Xu1https://orcid.org/0000-0002-5158-6203Zongyong Cui2https://orcid.org/0000-0003-1155-786XSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaFace recognition (FR) with a single sample per person (SSPP) is one of the most challenging problems in computer vision. In this scenario, it is difficult to predict facial variation such as pose, illumination, and disguise due to the lack of enough training samples. Therefore, the development of the FR system with only a small number of training samples is hindered. To address this problem, this paper proposes a scheme combined transfer learning and sample expansion in feature space. First, it uses transfer learning by training a deep convolutional neural network on a common multi-sample face dataset and then applies the well-trained model to a target data set. Second, it proposes a sample expansion method in feature space called k class feature transfer (KCFT) to enrich intra-class variation information for a single-sample face feature. Compared with other expanding sample methods in the image domain, this method of expanding the samples in the feature domain is novel and easy to implement. Third, it trains a softmax classifier with expanded face features. The experimental results on ORL, FERET, and LFW face databases demonstrate the effectiveness and robustness of the proposed method for various facial variations.https://ieeexplore.ieee.org/document/8681088/Face recognitionsingle sampletransfer learningsample expansionfeature space |
| spellingShingle | Rui Min Shengping Xu Zongyong Cui Single-Sample Face Recognition Based on Feature Expansion IEEE Access Face recognition single sample transfer learning sample expansion feature space |
| title | Single-Sample Face Recognition Based on Feature Expansion |
| title_full | Single-Sample Face Recognition Based on Feature Expansion |
| title_fullStr | Single-Sample Face Recognition Based on Feature Expansion |
| title_full_unstemmed | Single-Sample Face Recognition Based on Feature Expansion |
| title_short | Single-Sample Face Recognition Based on Feature Expansion |
| title_sort | single sample face recognition based on feature expansion |
| topic | Face recognition single sample transfer learning sample expansion feature space |
| url | https://ieeexplore.ieee.org/document/8681088/ |
| work_keys_str_mv | AT ruimin singlesamplefacerecognitionbasedonfeatureexpansion AT shengpingxu singlesamplefacerecognitionbasedonfeatureexpansion AT zongyongcui singlesamplefacerecognitionbasedonfeatureexpansion |