Toward Practical Smile Detection
Machine learning approaches have produced some of the highest reported performances for facial expression recognition. However, to date, nearly all automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled lighting...
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| Published in | IEEE transactions on pattern analysis and machine intelligence Vol. 31; no. 11; pp. 2106 - 2111 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Los Alamitos, CA
IEEE
01.11.2009
IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0162-8828 1939-3539 1939-3539 |
| DOI | 10.1109/TPAMI.2009.42 |
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| Abstract | Machine learning approaches have produced some of the highest reported performances for facial expression recognition. However, to date, nearly all automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled lighting conditions on a relatively small number of subjects. This paper explores whether current machine learning methods can be used to develop an expression recognition system that operates reliably in more realistic conditions. We explore the necessary characteristics of the training data set, image registration, feature representation, and machine learning algorithms. A new database, GENKI, is presented which contains pictures, photographed by the subjects themselves, from thousands of different people in many different real-world imaging conditions. Results suggest that human-level expression recognition accuracy in real-life illumination conditions is achievable with machine learning technology. However, the data sets currently used in the automatic expression recognition literature to evaluate progress may be overly constrained and could potentially lead research into locally optimal algorithmic solutions. |
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| AbstractList | Machine learning approaches have produced some of the highest reported performances for facial expression recognition. However, to date, nearly all automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled lighting conditions on a relatively small number of subjects. This paper explores whether current machine learning methods can be used to develop an expression recognition system that operates reliably in more realistic conditions. We explore the necessary characteristics of the training data set, image registration, feature representation, and machine learning algorithms. A new database, GENKI, is presented which contains pictures, photographed by the subjects themselves, from thousands of different people in many different real-world imaging conditions. Results suggest that human-level expression recognition accuracy in real-life illumination conditions is achievable with machine learning technology. However, the data sets currently used in the automatic expression recognition literature to evaluate progress may be overly constrained and could potentially lead research into locally optimal algorithmic solutions. Machine learning approaches have produced some of the highest reported performances for facial expression recognition. However, to date, nearly all automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled lighting conditions on a relatively small number of subjects. This paper explores whether current machine learning methods can be used to develop an expression recognition system that operates reliably in more realistic conditions. We explore the necessary characteristics of the training data set, image registration, feature representation, and machine learning algorithms. A new database, GENKI, is presented which contains pictures, photographed by the subjects themselves, from thousands of different people in many different real-world imaging conditions. Results suggest that human-level expression recognition accuracy in real-life illumination conditions is achievable with machine learning technology. However, the data sets currently used in the automatic expression recognition literature to evaluate progress may be overly constrained and could potentially lead research into locally optimal algorithmic solutions.Machine learning approaches have produced some of the highest reported performances for facial expression recognition. However, to date, nearly all automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled lighting conditions on a relatively small number of subjects. This paper explores whether current machine learning methods can be used to develop an expression recognition system that operates reliably in more realistic conditions. We explore the necessary characteristics of the training data set, image registration, feature representation, and machine learning algorithms. A new database, GENKI, is presented which contains pictures, photographed by the subjects themselves, from thousands of different people in many different real-world imaging conditions. Results suggest that human-level expression recognition accuracy in real-life illumination conditions is achievable with machine learning technology. However, the data sets currently used in the automatic expression recognition literature to evaluate progress may be overly constrained and could potentially lead research into locally optimal algorithmic solutions. [...] to date, nearly all automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled lighting conditions on a relatively small number of subjects. |
| Author | Whitehill, J. Littlewort, G. Fasel, I. Movellan, J. Bartlett, M. |
| Author_xml | – sequence: 1 givenname: J. surname: Whitehill fullname: Whitehill, J. organization: Machine Perception Lab., Univ. of California, San Diego, La Jolla, CA, USA – sequence: 2 givenname: G. surname: Littlewort fullname: Littlewort, G. organization: Machine Perception Lab., Univ. of California, San Diego, La Jolla, CA, USA – sequence: 3 givenname: I. surname: Fasel fullname: Fasel, I. organization: Dept. of Comput. Sci., Univ. of Arizona, Tucson, AZ, USA – sequence: 4 givenname: M. surname: Bartlett fullname: Bartlett, M. organization: Machine Perception Lab., Univ. of California, San Diego, La Jolla, CA, USA – sequence: 5 givenname: J. surname: Movellan fullname: Movellan, J. organization: Machine Perception Lab., Univ. of California, San Diego, La Jolla, CA, USA |
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| Cites_doi | 10.1109/CVPR.2007.383059 10.1109/TSMCB.2004.825931 10.1142/S021969130400041X 10.1109/CVPR.2004.1315144 10.1109/ICCV.2003.1238646 10.1016/S1077-3142(03)00081-X 10.1109/CVPR.2006.259 10.1109/TPAMI.2003.1251154 10.1016/j.cviu.2004.07.014 10.1023/B:VISI.0000013087.49260.fb 10.1007/3-540-59119-2_166 10.1109/ICME.2005.1521424 10.1016/0031-3203(95)00067-4 10.1109/34.908962 10.1109/TPAMI.2007.1110 10.1109/ICPR.2004.1334680 10.1109/TPAMI.2005.93 10.1109/AFGR.2000.840611 10.1016/j.imavis.2005.09.011 10.1109/ICCV.1999.790410 10.1109/FGR.2006.72 10.1007/978-0-387-21606-5 10.4304/jmm.1.6.22-35 10.1016/S0031-3203(02)00052-3 10.1109/FGR.2006.55 10.1007/0-387-27257-7_12 10.1109/AMFG.2003.1240843 10.1214/aos/1016218223 10.1109/TPAMI.2008.52 10.1109/ICSMC.2004.1398369 10.1109/TPAMI.2005.90 10.1109/TPAMI.2007.1094 10.1109/34.799905 10.1109/34.895976 10.1109/TIP.2006.884954 |
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| Keywords | Computer vision Image databank Gesture recognition Algorithmics machine learning Date Optimization Face and gesture recognition Luminance Lighting Imaging Optimal solution Facies Database Illumination Automatic recognition Learning algorithm Artificial intelligence Facial expression |
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| References | ref13 ref35 ref12 ref34 ref15 ref37 ref14 ref36 ref31 ref11 ref33 ref10 ref32 Fenwick (ref40) 1991; 13 Pnevmatikakis (ref29) ref2 ref1 ref17 ref39 ref16 ref38 ref19 ref18 Movellan (ref30) 2005 ref24 Cortes (ref27) ref23 ref25 ref20 ref22 ref21 Ekman (ref26) 1976 ref28 ref8 ref7 ref9 ref4 ref3 ref6 ref5 |
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| SubjectTerms | Algorithms Applied sciences Artificial Intelligence Automatic control Biometry - methods Computer science; control theory; systems Computer Simulation computer vision Exact sciences and technology Face - anatomy & histology Face and gesture recognition Face recognition Facial Humans Illumination Image databases Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image registration Learning systems Lighting control Machine learning Machine learning algorithms Models, Biological Optimization Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Recognition Representations Reproducibility of Results Sensitivity and Specificity Smiling Spatial databases Studies Subtraction Technique Training data |
| Title | Toward Practical Smile Detection |
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