Effective recognition of facial micro-expressions with video motion magnification
Facial expression recognition has been intensively studied for decades, notably by the psychology community and more recently the pattern recognition community. What is more challenging, and the subject of more recent research, is the problem of recognizing subtle emotions exhibited by so-called mic...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Published: |
Springer
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/22818 |
| _version_ | 1848750977918697472 |
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| author | Wang, Y. See, J. Oh, Y. Phan, R. Rahulamathavan, Y. Ling, Huo Chong Tan, S. Li, X. |
| author_facet | Wang, Y. See, J. Oh, Y. Phan, R. Rahulamathavan, Y. Ling, Huo Chong Tan, S. Li, X. |
| author_sort | Wang, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Facial expression recognition has been intensively studied for decades, notably by the psychology community and more recently the pattern recognition community. What is more challenging, and the subject of more recent research, is the problem of recognizing subtle emotions exhibited by so-called micro-expressions. Recognizing a micro-expression is substantially more challenging than conventional expression recognition because these micro-expressions are only temporally exhibited in a fraction of a second and involve minute spatial changes. Until now, work in this field is at a nascent stage, with only a few existing micro-expression databases and methods. In this article, we propose a new micro-expression recognition approach based on the Eulerian motion magnification technique, which could reveal the hidden information and accentuate the subtle changes in micro-expression motion. Validation of our proposal was done on the recently proposed CASME II dataset in comparison with baseline and state-of-the-art methods. We achieve a good recognition accuracy of up to 75.30 % by using leave-one-out cross validation evaluation protocol. Extensive experiments on various factors at play further demonstrate the effectiveness of our proposed approach. |
| first_indexed | 2025-11-14T07:45:25Z |
| format | Journal Article |
| id | curtin-20.500.11937-22818 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:45:25Z |
| publishDate | 2017 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-228182018-03-29T09:06:47Z Effective recognition of facial micro-expressions with video motion magnification Wang, Y. See, J. Oh, Y. Phan, R. Rahulamathavan, Y. Ling, Huo Chong Tan, S. Li, X. Facial expression recognition has been intensively studied for decades, notably by the psychology community and more recently the pattern recognition community. What is more challenging, and the subject of more recent research, is the problem of recognizing subtle emotions exhibited by so-called micro-expressions. Recognizing a micro-expression is substantially more challenging than conventional expression recognition because these micro-expressions are only temporally exhibited in a fraction of a second and involve minute spatial changes. Until now, work in this field is at a nascent stage, with only a few existing micro-expression databases and methods. In this article, we propose a new micro-expression recognition approach based on the Eulerian motion magnification technique, which could reveal the hidden information and accentuate the subtle changes in micro-expression motion. Validation of our proposal was done on the recently proposed CASME II dataset in comparison with baseline and state-of-the-art methods. We achieve a good recognition accuracy of up to 75.30 % by using leave-one-out cross validation evaluation protocol. Extensive experiments on various factors at play further demonstrate the effectiveness of our proposed approach. 2017 Journal Article http://hdl.handle.net/20.500.11937/22818 10.1007/s11042-016-4079-6 Springer restricted |
| spellingShingle | Wang, Y. See, J. Oh, Y. Phan, R. Rahulamathavan, Y. Ling, Huo Chong Tan, S. Li, X. Effective recognition of facial micro-expressions with video motion magnification |
| title | Effective recognition of facial micro-expressions with video motion magnification |
| title_full | Effective recognition of facial micro-expressions with video motion magnification |
| title_fullStr | Effective recognition of facial micro-expressions with video motion magnification |
| title_full_unstemmed | Effective recognition of facial micro-expressions with video motion magnification |
| title_short | Effective recognition of facial micro-expressions with video motion magnification |
| title_sort | effective recognition of facial micro-expressions with video motion magnification |
| url | http://hdl.handle.net/20.500.11937/22818 |