Auditory-Visual Speech Processing (AVSP) 2010

Hakone, Kanagawa, Japan
September 30-October 3, 2010

Exploring Visual Features Through Gabor Representations for Facial Expression Detection

Sien W. Chew (1), Patrick Lucey (2), Sridha Sridharan (1), Clinton Fookes (1)

(1) Image and Video Research Laboratory, Queensland University of Technology, Brisbane, Australia
(2) Robotics Institute, Carnegie Mellon University and Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA

Gabor representations have been widely used in facial analysis (face recognition, face detection and facial expression detection) due to their biological relevance and computational properties. Two popular Gabor representations used in literature are: 1) Log-Gabor and 2) Gabor energy filters. Even though these representations are somewhat similar, they also have distinct differences as the Log-Gabor filters mimic the simple cells in the visual cortex while the Gabor energy filters emulate the complex cells, which causes subtle differences in the responses. In this paper, we analyze the difference between these two Gabor representations and quantify these differences on the task of facial action unit (AU) detection. In our experiments conducted on the Cohn-Kanade dataset, we report an average area underneath the ROC curve (A`) of 92.60% across 17 AUs for the Gabor energy filters, while the Log-Gabor representation achieved an average A` of 96.11%. This result suggests that small spatial differences that the Log-Gabor filters pick up on are more useful for AU detection than the differences in contours and edges that the Gabor energy filters extract.

Index Terms: action unit detection, AdaBoost feature selection, Log-Gabor filter, Gabor energy filter

Full Paper

Bibliographic reference.  Chew, Sien W. / Lucey, Patrick / Sridharan, Sridha / Fookes, Clinton (2010): "Exploring visual features through Gabor representations for facial expression detection", In AVSP-2010, paper P7.