Automatic Facial Expression Recognition Using Linear and Nonlinear Holistic Spatial Analysis

This paper is engaged in the holistic spatial analysis on facial expression images. We present a systematic comparison of machine learning methods applied to the problem of automatic facial expression recognition, including supervised and unsupervised subspace analysis, SVM classifier and their nonlinear versions. Image-based holistic spatial analysis is more adaptive to recognition task in that it automatically learns the inner structure of training samples and extracts the most pertinent features for classification. Nonlinear analysis methods which could extract higher order dependencies among input patterns are supposed to promote the performance of classification. Surprisingly, the linear classifiers outperformed their nonlinear versions in our experiments. We proposed a new feature selection method named the Weighted Saliency Maps(WSM). Compared to other feature selection schemes such as Adaboost and PCA, WSM has the advantage of being simple, fast and flexible.