Dynamic Facial Expression Recognition With Atlas Construction and Sparse Representation
In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of dif...
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| Published in | IEEE transactions on image processing Vol. 25; no. 5; pp. 1977 - 1992 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.05.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Online Access | Get full text |
| ISSN | 1057-7149 1941-0042 1941-0042 |
| DOI | 10.1109/TIP.2016.2537215 |
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| Abstract | In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison. |
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| AbstractList | In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison. In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison.In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison. |
| Author | Yimo Guo Guoying Zhao Pietikainen, Matti |
| Author_xml | – sequence: 1 surname: Yimo Guo fullname: Yimo Guo email: yimoguo@gmail.com organization: Dept. of Comput. Sci. & Eng., Univ. of Oulu, Oulu, Finland – sequence: 2 surname: Guoying Zhao fullname: Guoying Zhao email: gyzhao@ee.oulu.fi organization: Dept. of Comput. Sci. & Eng., Univ. of Oulu, Oulu, Finland – sequence: 3 givenname: Matti surname: Pietikainen fullname: Pietikainen, Matti email: mkp@ee.oulu.fi organization: Dept. of Comput. Sci. & Eng., Univ. of Oulu, Oulu, Finland |
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| Cites_doi | 10.1109/CVPR.2004.1315264 10.1109/TSMCB.2005.859075 10.1016/j.imavis.2011.12.003 10.1109/MMUL.2012.26 10.1109/CVPR.2013.439 10.1109/42.796284 10.1109/TPAMI.2002.1017623 10.1145/2663204.2666277 10.1023/B:VISI.0000029664.99615.94 10.1007/978-1-4419-8853-9 10.1109/TPAMI.2005.188 10.1109/ICIP.2010.5650670 10.1109/CVPR.2012.6247876 10.1145/2663204.2666278 10.1109/TPAMI.2015.2392774 10.1109/TPAMI.2007.1110 10.1109/TPAMI.2008.79 10.1109/TPAMI.2006.34 10.1109/TPAMI.2005.93 10.1016/j.patcog.2013.09.023 10.1109/AFGR.1998.670965 10.1109/CVPR.2005.177 10.1109/TPAMI.2010.50 10.1016/j.neuroimage.2004.07.068 10.1007/978-3-642-33709-3_45 10.1016/S1361-8415(01)80026-8 10.1007/s11263-010-0380-4 10.1023/B:VISI.0000043755.93987.aa 10.1109/ICME.2005.1521424 10.1016/S1077-3142(03)00081-X 10.1109/TPAMI.2006.10 10.1109/CVPRW.2010.5543262 10.1016/j.imavis.2008.08.005 10.1109/CVPR.2014.426 10.1111/j.2517-6161.1996.tb02080.x 10.5772/4841 10.1109/TSMCB.2012.2200675 10.1109/TPAMI.2013.141 10.1109/TPAMI.2008.52 10.1145/2663204.2666275 10.1016/j.imavis.2005.08.006 10.1109/CVPR.2005.297 10.1016/S0031-3203(02)00052-3 10.1023/B:VISI.0000013087.49260.fb 10.1109/TPAMI.2010.107 10.1023/A:1011161132514 10.1109/TPAMI.2009.193 10.1109/CVPR.2010.5540138 10.1109/ACII.2015.7344636 10.1145/2663204.2666274 10.1016/j.imavis.2014.02.008 10.1109/CVPR.2013.75 10.1109/CVPR.2008.4587523 10.1109/FG.2011.5771364 10.1109/CVPR.2014.226 |
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| References_xml | – start-page: 2887 year: 2012 ident: ref31 article-title: Face alignment by explicit shape regression publication-title: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit – ident: ref21 doi: 10.1109/CVPR.2004.1315264 – ident: ref19 doi: 10.1109/TSMCB.2005.859075 – ident: ref44 doi: 10.1016/j.imavis.2011.12.003 – start-page: 1 year: 2008 ident: ref20 article-title: Facial expression recognition using encoded dynamic features publication-title: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit – ident: ref52 doi: 10.1109/MMUL.2012.26 – ident: ref14 doi: 10.1109/CVPR.2013.439 – ident: ref27 doi: 10.1109/42.796284 – ident: ref4 doi: 10.1109/TPAMI.2002.1017623 – ident: ref56 doi: 10.1145/2663204.2666277 – ident: ref25 doi: 10.1023/B:VISI.0000029664.99615.94 – ident: ref40 doi: 10.1007/978-1-4419-8853-9 – ident: ref26 doi: 10.1109/TPAMI.2005.188 – ident: ref16 doi: 10.1109/ICIP.2010.5650670 – ident: ref45 doi: 10.1109/CVPR.2012.6247876 – ident: ref57 doi: 10.1145/2663204.2666278 – volume: 37 start-page: 2146 year: 2015 ident: ref11 article-title: Spatiotemporal directional number transitional graph for dynamic texture recognition publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2015.2392774 – ident: ref7 doi: 10.1109/TPAMI.2007.1110 – ident: ref38 doi: 10.1109/TPAMI.2008.79 – ident: ref6 doi: 10.1109/TPAMI.2006.34 – ident: ref9 doi: 10.1109/TPAMI.2005.93 – ident: ref12 doi: 10.1016/j.patcog.2013.09.023 – ident: ref22 doi: 10.1109/AFGR.1998.670965 – ident: ref24 doi: 10.1109/CVPR.2005.177 – ident: ref10 doi: 10.1109/TPAMI.2010.50 – year: 2009 ident: ref48 article-title: Beyond pixels: Exploring new representations and applications for motion analysis – ident: ref35 doi: 10.1016/j.neuroimage.2004.07.068 – ident: ref17 doi: 10.1007/978-3-642-33709-3_45 – ident: ref33 doi: 10.1016/S1361-8415(01)80026-8 – ident: ref23 doi: 10.1007/s11263-010-0380-4 – ident: ref15 doi: 10.1023/B:VISI.0000043755.93987.aa – ident: ref42 doi: 10.1109/ICME.2005.1521424 – ident: ref8 doi: 10.1016/S1077-3142(03)00081-X – ident: ref49 doi: 10.1109/TPAMI.2006.10 – ident: ref41 doi: 10.1109/CVPRW.2010.5543262 – ident: ref60 doi: 10.1016/j.imavis.2008.08.005 – ident: ref46 doi: 10.1109/CVPR.2014.426 – volume: 58 start-page: 267 year: 1996 ident: ref39 article-title: Regression shrinkage and selection via the lasso publication-title: J R Statist Soc B (Methodological) doi: 10.1111/j.2517-6161.1996.tb02080.x – start-page: 2562 year: 2012 ident: ref13 article-title: Learning active facial patches for expression analysis publication-title: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit – ident: ref5 doi: 10.5772/4841 – ident: ref43 doi: 10.1109/TSMCB.2012.2200675 – ident: ref32 doi: 10.1109/TPAMI.2013.141 – ident: ref2 doi: 10.1109/TPAMI.2008.52 – ident: ref55 doi: 10.1145/2663204.2666275 – ident: ref18 doi: 10.1016/j.imavis.2005.08.006 – ident: ref3 doi: 10.1109/CVPR.2005.297 – ident: ref1 doi: 10.1016/S0031-3203(02)00052-3 – ident: ref50 doi: 10.1023/B:VISI.0000013087.49260.fb – ident: ref30 doi: 10.1109/TPAMI.2010.107 – ident: ref34 doi: 10.1023/A:1011161132514 – ident: ref36 doi: 10.1109/TPAMI.2009.193 – ident: ref47 doi: 10.1109/CVPR.2010.5540138 – ident: ref53 doi: 10.1109/ACII.2015.7344636 – ident: ref58 doi: 10.1145/2663204.2666274 – ident: ref59 doi: 10.1016/j.imavis.2014.02.008 – ident: ref28 doi: 10.1109/CVPR.2013.75 – start-page: 24 year: 2008 ident: ref37 article-title: Comparing algorithms for diffeomorphic registration: Stationary lddmm and diffeomorphic demons publication-title: 2nd MICCAI Workshop on Mathematical Foundations of Computational Anatomy (MFCA) – ident: ref29 doi: 10.1109/CVPR.2008.4587523 – ident: ref51 doi: 10.1109/FG.2011.5771364 – ident: ref54 doi: 10.1109/CVPR.2014.226 |
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| SubjectTerms | Diffeomorphic Growth Model Dynamic Facial Expression Recognition Dynamics Face recognition Feature extraction Groupwise Registration Image processing Image recognition Image registration Mathematical models Object recognition Representations Shape Sociology Sparse Representation Statistics |
| Title | Dynamic Facial Expression Recognition With Atlas Construction and Sparse Representation |
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