Monogenic Riesz wavelet representation for micro-expression recognition
dc.contributor.author | Oh, Y. | |
dc.contributor.author | Le Ngo, A. | |
dc.contributor.author | See, J. | |
dc.contributor.author | Liong, S. | |
dc.contributor.author | Phan, R. | |
dc.contributor.author | Ling, Huo Chong | |
dc.date.accessioned | 2018-12-13T09:08:28Z | |
dc.date.available | 2018-12-13T09:08:28Z | |
dc.date.created | 2018-12-12T02:47:03Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Oh, Y. and Le Ngo, A. and See, J. and Liong, S. and Phan, R. and Ling, H.C. 2015. Monogenic Riesz wavelet representation for micro-expression recognition, pp. 1237-1241. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/71009 | |
dc.identifier.doi | 10.1109/ICDSP.2015.7252078 | |
dc.description.abstract |
© 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. | |
dc.title | Monogenic Riesz wavelet representation for micro-expression recognition | |
dc.type | Conference Paper | |
dcterms.source.volume | 2015-September | |
dcterms.source.startPage | 1237 | |
dcterms.source.endPage | 1241 | |
dcterms.source.title | International Conference on Digital Signal Processing, DSP | |
dcterms.source.series | International Conference on Digital Signal Processing, DSP | |
dcterms.source.isbn | 9781479980581 | |
curtin.department | Curtin Malaysia | |
curtin.accessStatus | Fulltext not available |
Files in this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |