Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary
Sparse Representation-Based Classification (SRC) is a face recognition breakthrough in recent years which has successfully addressed the recognition problem with sufficient training images of each gallery subject. In this paper, we extend SRC to applications where there are very few, or even a singl...
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| Published in | IEEE transactions on pattern analysis and machine intelligence Vol. 34; no. 9; pp. 1864 - 1870 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Los Alamitos, CA
IEEE
01.09.2012
IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0162-8828 1939-3539 2160-9292 1939-3539 |
| DOI | 10.1109/TPAMI.2012.30 |
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| Abstract | Sparse Representation-Based Classification (SRC) is a face recognition breakthrough in recent years which has successfully addressed the recognition problem with sufficient training images of each gallery subject. In this paper, we extend SRC to applications where there are very few, or even a single, training images per subject. Assuming that the intraclass variations of one subject can be approximated by a sparse linear combination of those of other subjects, Extended Sparse Representation-Based Classifier (ESRC) applies an auxiliary intraclass variant dictionary to represent the possible variation between the training and testing images. The dictionary atoms typically represent intraclass sample differences computed from either the gallery faces themselves or the generic faces that are outside the gallery. Experimental results on the AR and FERET databases show that ESRC has better generalization ability than SRC for undersampled face recognition under variable expressions, illuminations, disguises, and ages. The superior results of ESRC suggest that if the dictionary is properly constructed, SRC algorithms can generalize well to the large-scale face recognition problem, even with a single training image per class. |
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| AbstractList | Sparse Representation-Based Classification (SRC) is a face recognition breakthrough in recent years which has successfully addressed the recognition problem with sufficient training images of each gallery subject. In this paper, we extend SRC to applications where there are very few, or even a single, training images per subject. Assuming that the intraclass variations of one subject can be approximated by a sparse linear combination of those of other subjects, Extended Sparse Representation-Based Classifier (ESRC) applies an auxiliary intraclass variant dictionary to represent the possible variation between the training and testing images. The dictionary atoms typically represent intraclass sample differences computed from either the gallery faces themselves or the generic faces that are outside the gallery. Experimental results on the AR and FERET databases show that ESRC has better generalization ability than SRC for undersampled face recognition under variable expressions, illuminations, disguises, and ages. The superior results of ESRC suggest that if the dictionary is properly constructed, SRC algorithms can generalize well to the large-scale face recognition problem, even with a single training image per class. Sparse Representation-Based Classification (SRC) is a face recognition breakthrough in recent years which has successfully addressed the recognition problem with sufficient training images of each gallery subject. In this paper, we extend SRC to applications where there are very few, or even a single, training images per subject. Assuming that the intraclass variations of one subject can be approximated by a sparse linear combination of those of other subjects, Extended Sparse Representation-Based Classifier (ESRC) applies an auxiliary intraclass variant dictionary to represent the possible variation between the training and testing images. The dictionary atoms typically represent intraclass sample differences computed from either the gallery faces themselves or the generic faces that are outside the gallery. Experimental results on the AR and FERET databases show that ESRC has better generalization ability than SRC for undersampled face recognition under variable expressions, illuminations, disguises, and ages. The superior results of ESRC suggest that if the dictionary is properly constructed, SRC algorithms can generalize well to the large-scale face recognition problem, even with a single training image per class.Sparse Representation-Based Classification (SRC) is a face recognition breakthrough in recent years which has successfully addressed the recognition problem with sufficient training images of each gallery subject. In this paper, we extend SRC to applications where there are very few, or even a single, training images per subject. Assuming that the intraclass variations of one subject can be approximated by a sparse linear combination of those of other subjects, Extended Sparse Representation-Based Classifier (ESRC) applies an auxiliary intraclass variant dictionary to represent the possible variation between the training and testing images. The dictionary atoms typically represent intraclass sample differences computed from either the gallery faces themselves or the generic faces that are outside the gallery. Experimental results on the AR and FERET databases show that ESRC has better generalization ability than SRC for undersampled face recognition under variable expressions, illuminations, disguises, and ages. The superior results of ESRC suggest that if the dictionary is properly constructed, SRC algorithms can generalize well to the large-scale face recognition problem, even with a single training image per class. |
| Author | Jun Guo Jiani Hu Weihong Deng |
| Author_xml | – sequence: 1 givenname: Weihong surname: Deng fullname: Deng, Weihong email: whdeng@bupt.edu.cn organization: Beijing University of Posts and Telecommunications, Beijing, China. whdeng@bupt.edu.cn – sequence: 2 givenname: Jiani surname: Hu fullname: Hu, Jiani – sequence: 3 givenname: Jun surname: Guo fullname: Guo, Jun |
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| Cites_doi | 10.1109/TIT.2008.929958 10.1109/TIP.2002.999679 10.1093/imanum/20.3.389 10.1109/TIP.2006.881969 10.1109/34.598228 10.1126/science.1157523 10.1109/TPAMI.2007.70783 10.1016/j.patcog.2009.12.026 10.1109/ICIP.2010.5651933 10.1109/34.879790 10.1109/TPAMI.2011.112 10.1016/j.patcog.2009.12.004 10.1109/TPAMI.2008.79 10.1109/34.531802 10.18772/10539/20690 10.1109/TPAMI.2005.55 10.1109/CVPR.2009.5206654 10.1109/34.927464 |
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| Title | Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary |
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