Joint representation classification for collective face recognition
In recent years, many representation based classifications have been proposed and widely used in face recognition. However, these methods code and classify testing images separately even for image-set of the same subject. This scheme utilizes only an individual representation rather than the collect...
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| Published in | Pattern recognition Vol. 63; pp. 182 - 192 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.03.2017
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| Online Access | Get full text |
| ISSN | 0031-3203 1873-5142 |
| DOI | 10.1016/j.patcog.2016.10.004 |
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| Abstract | In recent years, many representation based classifications have been proposed and widely used in face recognition. However, these methods code and classify testing images separately even for image-set of the same subject. This scheme utilizes only an individual representation rather than the collective one to classify such a set of images, doing so obviously ignores the correlation among the given set of images. In this paper, a joint representation classification (JRC) for collective face recognition is presented. JRC takes the correlation of multiple images as well as a single representation into account. Even for an image-set mixed with different subjects, JRC codes all the testing images over the base images simultaneously to facilitate recognition. To this end, the testing images are aligned into a matrix and the joint representation coding is formulated as a generalized l2,q−l2,p matrix minimization problem. A unified algorithm, named by iterative quadratic method (IQM), and its practical implementation are developed specially to solve the induced optimization problem for any q∈[1,2] and p∈(0,2]. Experimental results on three public databases show that the JRC with practical IQM not only saves much computational cost but also achieves better performance in collective face recognition than state-of-the-art methods.
•A joint representation classification (JRC) is presented.•JRC codes the collective images simultaneously.•Joint representation coding is formulated to a mixed l2,q−l2,p minimization.•A unified method is developed and its convergence is established.•Experiments confirm the collective face recognition performance of JRC over state-of-the-arts. |
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| AbstractList | In recent years, many representation based classifications have been proposed and widely used in face recognition. However, these methods code and classify testing images separately even for image-set of the same subject. This scheme utilizes only an individual representation rather than the collective one to classify such a set of images, doing so obviously ignores the correlation among the given set of images. In this paper, a joint representation classification (JRC) for collective face recognition is presented. JRC takes the correlation of multiple images as well as a single representation into account. Even for an image-set mixed with different subjects, JRC codes all the testing images over the base images simultaneously to facilitate recognition. To this end, the testing images are aligned into a matrix and the joint representation coding is formulated as a generalized l2,q−l2,p matrix minimization problem. A unified algorithm, named by iterative quadratic method (IQM), and its practical implementation are developed specially to solve the induced optimization problem for any q∈[1,2] and p∈(0,2]. Experimental results on three public databases show that the JRC with practical IQM not only saves much computational cost but also achieves better performance in collective face recognition than state-of-the-art methods.
•A joint representation classification (JRC) is presented.•JRC codes the collective images simultaneously.•Joint representation coding is formulated to a mixed l2,q−l2,p minimization.•A unified method is developed and its convergence is established.•Experiments confirm the collective face recognition performance of JRC over state-of-the-arts. |
| Author | Chen, Songcan Wang, Liping |
| Author_xml | – sequence: 1 givenname: Liping surname: Wang fullname: Wang, Liping email: wlpmath@nuaa.edu.cn organization: Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China – sequence: 2 givenname: Songcan surname: Chen fullname: Chen, Songcan email: s.chen@nuaa.edu.cn organization: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
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| Cites_doi | 10.1109/TNN.2011.2157521 10.1109/TPAMI.2008.79 10.1109/ICASSP.2008.4518498 10.1137/S1052623494266365 10.1109/JSTSP.2007.910971 10.1109/ICCV.2013.146 10.1109/TPAMI.2013.109 10.1093/imanum/8.1.141 10.1109/CVPR.2008.4587640 10.1007/s10107-005-0595-2 10.1093/imanum/22.1.1 10.1109/TIT.2010.2048473 10.1109/TIFS.2014.2324277 10.1109/34.927464 10.1007/s10589-014-9648-x 10.1109/34.254061 10.1093/imanum/13.3.321 10.1109/LSP.2007.898300 10.1109/CVPR.2009.5206654 10.1007/s10107-012-0613-0 10.1007/s10589-013-9553-8 10.1109/TPAMI.2007.1037 10.1109/ICCV.2011.6126277 10.1109/TPAMI.2005.92 10.1007/s00041-008-9045-x 10.1109/TPAMI.2011.282 10.1109/TIP.2012.2206039 10.1007/978-3-642-15561-1_1 10.1007/s10107-013-0722-4 |
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