Facial expression recognition with dynamic Gabor volume feature

Junkai Chen, Zheru Chi, Hong Fu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


Facial expression recognition is a long standing problem in affective computing community. A key step is extracting effective features from face images. Gabor filters have been widely used for this purpose. However, a big challenge for Gabor filters is its high dimensionality. In this paper, we propose an efficient feature called dynamic Gabor volume feature (DGVF) based on Gabor filters while with a lower dimensionality for facial expression recognition. In our approach, we first apply Gabor filters with multi-scale and multi-orientation to extract different Gabor faces. And these Gabor faces are arranged into a 3-D volume and Histograms of Oriented Gradients from Three Orthogonal Planes (HOG-TOP) are further employed to encode the 3-D volume in a compact way. Finally, SVM is trained to perform the classification. The experiments conducted on the Extended Cohn-Kanade (CK+) Dataset show that the proposed DGVF is robust to capture and represent the facial appearance features. And our method also achieves a superior performance compared with the other state-of-the-art methods.
Original languageEnglish
Title of host publication2016 IEEE 18th International Workshop on Multimedia Signal Processing, MMSP 2016
ISBN (Electronic)9781509037247
Publication statusPublished - 10 Jan 2017
Event18th IEEE International Workshop on Multimedia Signal Processing, MMSP 2016 - Montreal, Canada
Duration: 21 Sep 201623 Sep 2016


Conference18th IEEE International Workshop on Multimedia Signal Processing, MMSP 2016

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology

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