Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space an...
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| Published in | International journal of computer vision Vol. 25; no. 1; pp. 23 - 48 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Heidelberg
Springer
01.10.1997
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0920-5691 1573-1405 |
| DOI | 10.1023/A:1007977618277 |
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| Abstract | This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid facial motions but also provide a concise description of the motion in terms of a small number of parameters. These parameters are intuitively related to the motion of facial features during facial expressions and we show how expressions such as anger, happiness, surprise, fear, disgust, and sadness can be recognized from the local parametric motions in the presence of significant head motion. The motion tracking and expression recognition approach performed with high accuracy in extensive laboratory experiments involving 40 subjects as well as in television and movie sequences. |
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| AbstractList | This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid facial motions but also provide a concise description of the motion in terms of a small number of parameters. These parameters are intuitively related to the motion of facial features during facial expressions and we show how expressions such as anger, happiness, surprise, fear, disgust, and sadness can be recognized from the local parametric motions in the presence of significant head motion. The motion tracking and expression recognition approach performed with high accuracy in extensive laboratory experiments involving 40 subjects as well as in television and movie sequences. This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid facial motions but also provide a concise description of the motion in terms of a small number of parameters. These parameters are intuitively related to the motion of facial features during facial expressions and we show how expressions such as anger, happiness, surprise, fear, disgust, and sadness can be recognized from the local parametric motions in the presence of significant head motion. The motion tracking and expression recognition approach performed with high accuracy in extensive laboratory experiments involving 40 subjects as well as in television and movie sequences.[PUBLICATION ABSTRACT] |
| Author | Black, Michael J. Yacoob, Yaser |
| Author_xml | – sequence: 1 givenname: Michael J. surname: Black fullname: Black, Michael J. – sequence: 2 givenname: Yaser surname: Yacoob fullname: Yacoob, Yaser |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=2051081$$DView record in Pascal Francis |
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| Cites_doi | 10.1037/0022-3514.37.11.2049 10.1109/MNRAO.1994.346257 10.1109/34.216730 10.1006/cviu.1996.0006 10.1098/rstb.1992.0008 10.1109/CVPR.1994.323812 10.1109/ICCV.1993.378214 10.1016/0031-3203(93)90173-T 10.1080/713819112 10.1109/TPAMI.1985.4767678 10.1109/34.216724 10.1109/34.216726 10.1109/CVPR.1993.341070 10.1007/3-540-55426-2_27 10.1109/MNRAO.1994.346232 10.1109/CVPR.1993.340966 10.1109/CVPR.1989.37836 10.1145/192161.192197 10.1007/3-540-55426-2_21 10.1109/CVPR.1994.323813 |
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| Keywords | Parameter estimation Motion estimation Image processing Image sequence Estimator robustness Pattern recognition Experimental study Face Optical flow |
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| SubjectTerms | Algorithmics. Computability. Computer arithmetics Applied sciences Artificial intelligence Computer science; control theory; systems Estimation Exact sciences and technology Human computer interaction Image understanding Mathematical models Object recognition Optical flows Pattern recognition. Digital image processing. Computational geometry Theoretical computing |
| Title | Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion |
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