Facial emotion recognition with anisotropic inhibited Gabor energy histograms
Albert C. Cruz, Bir Bhanu, Ninad S. Thakoor · 2013
State-of-the-art approaches have yet to deliver a feature representation for facial emotion recognition that can be applied to non-trivial unconstrained, continuous video data sets. Initially, research advanced with the use of Gabor energy filters. However, in recent work more attention has been given to other features. Gabor energy filters lack generalization needed in unconstrained situations. Additionally, they result in an undesirably high feature vector dimensionality. Nontrivial data sets have millions of samples; feature vectors must be as low dimensional as possible. We propose a novel texture feature based on Gabor energy filters that offers generalization with a background texture suppression component and is as compact as possible due to a maximal response representation and local histograms. We improve performance on the non-trivial Audio/Visual Emotion Challenge 2012 grandchallenge data set.