Monogenic Riesz wavelet representation for micro-expression recognition

Yee-Hui Oh, Anh Cat Le Ngo, John See, Sze-Teng Liong, Raphael C. W. Phan, Huo-Chong Ling

Research output: Chapter in Book/Report/Conference proceedingConference contribution

51 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Digital Signal Processing (DSP)
Number of pages5
ISBN (Electronic)9781479980581
Publication statusPublished - 10 Sept 2015
Event2015 IEEE International Conference on Digital Signal Processing - Singapore, Singapore
Duration: 21 Jul 201524 Jul 2015


Conference2015 IEEE International Conference on Digital Signal Processing
Abbreviated titleDSP 2015


  • facial micro-expressions
  • Monogenic signal
  • quaternion representation
  • Riesz wavelet transform

ASJC Scopus subject areas

  • Signal Processing


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