Multiple classifier systems for the classificatio of audio-visual emotional states
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
A robust feature extraction method for human facial expressions recognition systems
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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The Gaussian mixture model (GMM) super vector approach is a well known technique in the domain of speech processing, e.g. speaker verification and audio segmentation. In this paper we apply this approach to video data in order to recognize human facial expressions. Three different image feature types (optical flow histograms, orientation histograms and principal components) from four pre-selected regions of the human’s face image were extracted and GMM super-vectors of the feature channels per sequence were constructed. Support vector machines (SVM) were trained using these super vectors for every channel separately and its results were combined using classifier fusion techniques. Thus, the performance of the classifier could be improved compared to the best individual classifier.