A Multiple Classifier System Approach for Facial Expressions in Image Sequences Utilizing GMM Supervectors

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 ???ow 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.