The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior
Abstract
We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions. We report results on a series of experiments comparing recognition engines, including AdaBoost, support vector machines, linear discriminant analysis. We also explored feature selection techniques, including the use of AdaBoost for feature selection prior to classification by SVM or LDA. Best results were obtained by selecting a subset of Gabor filters using AdaBoost followed by classification with support vector machines. The system operates in real-time, and obtained 93% correct generalization to novel subjects for a 7-way forced choice on the Cohn-Kanade expression dataset. The outputs of the classifiers change smoothly as a function of time and thus can be used to measure facial expression dynamics. We applied the system to to fully automated recognition of facial actions (FACS). The present system classifies 17 action units, whether they occur singly or in combination with other actions, with a mean accuracy of 94.8%. We present preliminary results for applying this system to spontaneous facial expressions.
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Cites
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
Comprehensive database for facial expression analysis
Kanade, T., Cohn, J. F., & Tian, Y. (2002) · Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) · doi:10.1109/afgr.2000.840611 · Cited by 2,591
Robust real-time object detection.
Viola, P. A. (2001) · Cited by 2,340
Distortion invariant object recognition in the dynamic link architecture
Lades, M., Vorbruggen, J. C., Buhmann, J., Lange, J., von der Malsburg, C., Wurtz, R. P., & Konen, W. (1993) · IEEE Transactions on Computers · doi:10.1109/12.210173 · Cited by 1,805
Automatic analysis of facial expressions: the state of the art
Pantie, M., & Rothkrantz, L. J. M. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.895976 · Cited by 1,778 · cited 2×
“The approach to automatic FACS coding presented here, in addition to being fully automated, also differs from approaches such as [ 13 ] and [16] in that instead of designing special purpose image features for each facial action, we explore general purpose learning mechanisms for data-driven facial expression classific…”
Recognizing action units for facial expression analysis
Tian, Y.-I., Kanade, T., & Cohn, J. F. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908962 · Cited by 1,745
A brief introduction to boosting
Schapire, R. E. (1999) · Cited by 1,292
Donato, G., Bartlett, M. S., Hager, J. C., Ekman, P., & Sejnowski, T. J. (1999) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.799905 · Cited by 999
Self-citationFacial Action Recognition for Facial Expression Analysis From Static Face Images
Pantic, M., & Rothkrantz, L. J. M. (2004) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2004.825931 · Cited by 417
Cited by
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Shan, C., Gong, S., & McOwan, P. W. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.08.005 · Cited by 2,300
Compound facial expressions of emotion
Du, S., Tao, Y., & Martinez, A. M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1322355111 · Cited by 806
Lopes, A. T., de Aguiar, E., De Souza, A. F., & Oliveira-Santos, T. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.07.026 · Cited by 799
DISFA: A Spontaneous Facial Action Intensity Database
Mavadati, S. M., Mahoor, M. H., Bartlett, K., Trinh, P., & Cohn, J. F. (2013) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2013.4 · Cited by 793
Metadata
- Authors
- Bartlett, M. S., Littlewort, G., Frank, M., Lainscsek, C., Fasel, I., & Movellan, J.
- Venue
- 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
- Publisher
- IEEE
- Published
- 2005-07-27
- Volume
- 2
- Pages
- 568-573
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2102570318
- Cited by
- 624
- References
- 16
- FWCI
- 33.13
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Bartlett, M. S., Littlewort, G., Frank, M., Lainscsek, C., Fasel, I., & Movellan, J. (2005). Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). https://doi.org/10.1109/cvpr.2005.297
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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