Improved Cross-Label Suppression Dictionary Learning for Face Recognition
Cross-label suppression dictionary learning is an effective approach to preserve the label property for signal representation in face recognition. This paper presents a proposed improved dictionary learning algorithm, considering the tradeoffs between the operating time and the signal reconstruction...
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| Published in: | IEEE access Vol. 6; pp. 48716 - 48725 |
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| Format: | Journal Article |
| Language: | English |
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IEEE
01-01-2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | Cross-label suppression dictionary learning is an effective approach to preserve the label property for signal representation in face recognition. This paper presents a proposed improved dictionary learning algorithm, considering the tradeoffs between the operating time and the signal reconstruction residuals for the face recognition problem that combines an optimal loss function and the cross-label suppression supervised dictionary learning approach. Based on the relationship of the cost time of the dictionary learning algorithm and the residuals of the sparse representations, this paper attempts to select an optimal sparse coding dimension for the original signal to reduce the computational cost. Experiments on face recognition confirm that our proposed algorithm is able to achieve a desired classification results as well as obtain a considerably faster dictionary learning process. |
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| AbstractList | Cross-label suppression dictionary learning is an effective approach to preserve the label property for signal representation in face recognition. This paper presents a proposed improved dictionary learning algorithm, considering the tradeoffs between the operating time and the signal reconstruction residuals for the face recognition problem that combines an optimal loss function and the cross-label suppression supervised dictionary learning approach. Based on the relationship of the cost time of the dictionary learning algorithm and the residuals of the sparse representations, this paper attempts to select an optimal sparse coding dimension for the original signal to reduce the computational cost. Experiments on face recognition confirm that our proposed algorithm is able to achieve a desired classification results as well as obtain a considerably faster dictionary learning process. |
| Author | Zhou, Tian Yao, Jiming Wang, Lei Gui, Guan Yang, Sujuan |
| Author_xml | – sequence: 1 givenname: Tian surname: Zhou fullname: Zhou, Tian organization: National Engineering Research Center for Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, China – sequence: 2 givenname: Sujuan surname: Yang fullname: Yang, Sujuan organization: National Engineering Research Center for Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, China – sequence: 3 givenname: Lei surname: Wang fullname: Wang, Lei organization: National Engineering Research Center for Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, China – sequence: 4 givenname: Jiming surname: Yao fullname: Yao, Jiming organization: Global Energy Interconnection Research Institute Co., Ltd., Beijing, China – sequence: 5 givenname: Guan orcidid: 0000-0003-3888-2881 surname: Gui fullname: Gui, Guan email: guiguan@njupt.edu.cn organization: National Engineering Research Center for Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, China |
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| SubjectTerms | Algorithms compressive sensing computational complexity Computational modeling Computing costs Cross-label suppression Dictionaries dictionary learning Face recognition Machine learning Representations Signal reconstruction Time complexity Training |
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| Title | Improved Cross-Label Suppression Dictionary Learning for Face Recognition |
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