Sparse and Dense Hybrid Representation via Dictionary Decomposition for Face Recognition
Sparse representation provides an effective tool for classification under the conditions that every class has sufficient representative training samples and the training data are uncorrupted. These conditions may not hold true in many practical applications. Face identification is an example where w...
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| Vydáno v: | IEEE transactions on pattern analysis and machine intelligence Ročník 37; číslo 5; s. 1067 - 1079 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
IEEE
01.05.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0162-8828, 1939-3539, 2160-9292, 1939-3539 |
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| Abstract | Sparse representation provides an effective tool for classification under the conditions that every class has sufficient representative training samples and the training data are uncorrupted. These conditions may not hold true in many practical applications. Face identification is an example where we have a large number of identities but sufficient representative and uncorrupted training images cannot be guaranteed for every identity. A violation of the two conditions leads to a poor performance of the sparse representation-based classification (SRC). This paper addresses this critic issue by analyzing the merits and limitations of SRC. A sparse- and dense-hybrid representation (SDR) framework is proposed in this paper to alleviate the problems of SRC. We further propose a procedure of supervised low-rank (SLR) dictionary decomposition to facilitate the proposed SDR framework. In addition, the problem of the corrupted training data is also alleviated by the proposed SLR dictionary decomposition. The application of the proposed SDR-SLR approach in face recognition verifies its effectiveness and advancement to the field. Extensive experiments on benchmark face databases demonstrate that it consistently outperforms the state-of-the-art sparse representation based approaches and the performance gains are significant in most cases. |
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| AbstractList | Sparse representation provides an effective tool for classification under the conditions that every class has sufficient representative training samples and the training data are uncorrupted. These conditions may not hold true in many practical applications. Face identification is an example where we have a large number of identities but sufficient representative and uncorrupted training images cannot be guaranteed for every identity. A violation of the two conditions leads to a poor performance of the sparse representation-based classification (SRC). This paper addresses this critic issue by analyzing the merits and limitations of SRC. A sparse- and dense-hybrid representation (SDR) framework is proposed in this paper to alleviate the problems of SRC. We further propose a procedure of supervised low-rank (SLR) dictionary decomposition to facilitate the proposed SDR framework. In addition, the problem of the corrupted training data is also alleviated by the proposed SLR dictionary decomposition. The application of the proposed SDR-SLR approach in face recognition verifies its effectiveness and advancement to the field. Extensive experiments on benchmark face databases demonstrate that it consistently outperforms the state-of-the-art sparse representation based approaches and the performance gains are significant in most cases. Sparse representation provides an effective tool for classification under the conditions that every class has sufficient representative training samples and the training data are uncorrupted. These conditions may not hold true in many practical applications. Face identification is an example where we have a large number of identities but sufficient representative and uncorrupted training images cannot be guaranteed for every identity. A violation of the two conditions leads to a poor performance of the sparse representation-based classification (SRC). This paper addresses this critic issue by analyzing the merits and limitations of SRC. A sparse- and dense-hybrid representation (SDR) framework is proposed in this paper to alleviate the problems of SRC. We further propose a procedure of supervised low-rank (SLR) dictionary decomposition to facilitate the proposed SDR framework. In addition, the problem of the corrupted training data is also alleviated by the proposed SLR dictionary decomposition. The application of the proposed SDR-SLR approach in face recognition verifies its effectiveness and advancement to the field. Extensive experiments on benchmark face databases demonstrate that it consistently outperforms the state-of-the-art sparse representation based approaches and the performance gains are significant in most cases.Sparse representation provides an effective tool for classification under the conditions that every class has sufficient representative training samples and the training data are uncorrupted. These conditions may not hold true in many practical applications. Face identification is an example where we have a large number of identities but sufficient representative and uncorrupted training images cannot be guaranteed for every identity. A violation of the two conditions leads to a poor performance of the sparse representation-based classification (SRC). This paper addresses this critic issue by analyzing the merits and limitations of SRC. A sparse- and dense-hybrid representation (SDR) framework is proposed in this paper to alleviate the problems of SRC. We further propose a procedure of supervised low-rank (SLR) dictionary decomposition to facilitate the proposed SDR framework. In addition, the problem of the corrupted training data is also alleviated by the proposed SLR dictionary decomposition. The application of the proposed SDR-SLR approach in face recognition verifies its effectiveness and advancement to the field. Extensive experiments on benchmark face databases demonstrate that it consistently outperforms the state-of-the-art sparse representation based approaches and the performance gains are significant in most cases. |
| Author | Xudong Jiang Jian Lai |
| Author_xml | – sequence: 1 givenname: Xudong surname: Jiang fullname: Jiang, Xudong – sequence: 2 givenname: Jian surname: Lai fullname: Lai, Jian |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26353329$$D View this record in MEDLINE/PubMed |
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| Keywords | face recognition low-rank matrix recovery Sparse representation dictionary learning classification |
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| SubjectTerms | Algorithms Biometric Identification - methods Databases, Factual Dictionaries Face Face - anatomy & histology Face recognition Feature extraction Humans Image Processing, Computer-Assisted - methods Sparse matrices Training Training data Vectors |
| Title | Sparse and Dense Hybrid Representation via Dictionary Decomposition for Face Recognition |
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