Monogenic Riesz wavelet representation for micro-expression recognition
© 2015 IEEE. A monogenic signal is a two-dimensional analytical signal that provides the local information of magnitude, phase, and orientation. While it has been applied on the field of face and expression recognition [1], [2], [3], there are no known usages for subtle facial micro-expressions. In...
| Main Authors: | , , , , , |
|---|---|
| Format: | Conference Paper |
| Published: |
2015
|
| Online Access: | http://hdl.handle.net/20.500.11937/71009 |
| _version_ | 1848762365480271872 |
|---|---|
| author | Oh, Y. Le Ngo, A. See, J. Liong, S. Phan, R. Ling, Huo Chong |
| author_facet | Oh, Y. Le Ngo, A. See, J. Liong, S. Phan, R. Ling, Huo Chong |
| author_sort | Oh, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2015 IEEE. A monogenic signal is a two-dimensional analytical signal that provides the local information of magnitude, phase, and orientation. While it has been applied on the field of face and expression recognition [1], [2], [3], there are no known usages for subtle facial micro-expressions. In this paper, we propose a feature representation method which succinctly captures these three low-level components at multiple scales. Riesz wavelet transform is employed to obtain multi-scale monogenic wavelets, which are formulated by quaternion representation. Instead of summing up the multi-scale monogenic representations, we consider all monogenic representations across multiple scales as individual features. For classification, two schemes were applied to integrate these multiple feature representations: a fusion-based method which combines the features efficiently and discriminately using the ultra-fast, optimized Multiple Kernel Learning (UFO-MKL) algorithm; and concatenation-based method where the features are combined into a single feature vector and classified by a linear SVM. Experiments carried out on a recent spontaneous micro-expression database demonstrated the capability of the proposed method in outperforming the state-of-the-art monogenic signal approach to solving the micro-expression recognition problem. |
| first_indexed | 2025-11-14T10:46:25Z |
| format | Conference Paper |
| id | curtin-20.500.11937-71009 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:46:25Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-710092018-12-13T09:32:50Z Monogenic Riesz wavelet representation for micro-expression recognition Oh, Y. Le Ngo, A. See, J. Liong, S. Phan, R. Ling, Huo Chong © 2015 IEEE. A monogenic signal is a two-dimensional analytical signal that provides the local information of magnitude, phase, and orientation. While it has been applied on the field of face and expression recognition [1], [2], [3], there are no known usages for subtle facial micro-expressions. In this paper, we propose a feature representation method which succinctly captures these three low-level components at multiple scales. Riesz wavelet transform is employed to obtain multi-scale monogenic wavelets, which are formulated by quaternion representation. Instead of summing up the multi-scale monogenic representations, we consider all monogenic representations across multiple scales as individual features. For classification, two schemes were applied to integrate these multiple feature representations: a fusion-based method which combines the features efficiently and discriminately using the ultra-fast, optimized Multiple Kernel Learning (UFO-MKL) algorithm; and concatenation-based method where the features are combined into a single feature vector and classified by a linear SVM. Experiments carried out on a recent spontaneous micro-expression database demonstrated the capability of the proposed method in outperforming the state-of-the-art monogenic signal approach to solving the micro-expression recognition problem. 2015 Conference Paper http://hdl.handle.net/20.500.11937/71009 10.1109/ICDSP.2015.7252078 restricted |
| spellingShingle | Oh, Y. Le Ngo, A. See, J. Liong, S. Phan, R. Ling, Huo Chong Monogenic Riesz wavelet representation for micro-expression recognition |
| title | Monogenic Riesz wavelet representation for micro-expression recognition |
| title_full | Monogenic Riesz wavelet representation for micro-expression recognition |
| title_fullStr | Monogenic Riesz wavelet representation for micro-expression recognition |
| title_full_unstemmed | Monogenic Riesz wavelet representation for micro-expression recognition |
| title_short | Monogenic Riesz wavelet representation for micro-expression recognition |
| title_sort | monogenic riesz wavelet representation for micro-expression recognition |
| url | http://hdl.handle.net/20.500.11937/71009 |