Monogenic Riesz wavelet representation for micro-expression recognition
|dc.contributor.author||Le Ngo, A.|
|dc.contributor.author||Ling, Huo Chong|
|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.|
© 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 , , , 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|
|dcterms.source.title||International Conference on Digital Signal Processing, DSP|
|dcterms.source.series||International Conference on Digital Signal Processing, DSP|
|curtin.accessStatus||Fulltext not available|
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