Intelligent pixels of interest selection with application to facial expression recognition using multilayer perceptron

This paper presents an automatic way to discover pixels in a face image that improves the facial expression recognition results. Main contribution of our study is to provide a practical method to improve classification performance of classifiers by selecting best pixels of interest. Our method exhaustively searches for the best and worst feature window position from a set of face images among all possible combinations using MLP. Then, it creates a non-rectangular emotion mask for feature selection in supervised facial expression recognition problem. It eliminates irrelevant data and improves the classification performance using backward feature elimination. Experimental studies on GENKI, JAFFE and FERET databases showed that the proposed system improves the classification results by selecting the best pixels of interest.

► We discover pixels in an face image that improve emotional classification.
► We create emotion masks to improve classification using backward feature elimination.
► Small number of selected pixels outperforms full frame pixels.
► There is a high accuracy difference in very close feature windows.
► Positive emotions are likely to occur in the lower face.