Improving facial expression analysis using histograms of Log-Transformed Nonnegative Sparse Representation with a Spatial Pyramid Structure

Facial activity is the most direct signal for perceiving emotional states in people. Emotion analysis from facial displays has been attracted an increasing attention because of its wide applications from human-centered computing to neuropsychiatry. Recently, image representation based on sparse coding has shown promising results in facial expression recognition. In this paper, we introduce a novel image representation for facial expression analysis. Specifically, we propose to use the histograms of nonnegative sparse coded image features to represent a facial image. In order to capture fine appearance variations caused by facial expression, logarithmic transformation is further employed on each nonnegative sparse coded feature. In addition, the proposed Histograms of Log-Transformed Nonnegative Sparse Coding (HLNNSC) features are calculated and organized in a pyramid-like structure such that the spatial relationships among the features are captured and utilized to enhance the performance of facial expression recognition. Extensive experiments on the Cohn-Kanade database show that the proposed approach yields a significant improvement in facial expression recognition and outperforms the other sparse coding based baseline approaches. Furthermore, experimental results on the GEMEP-FERA2011 dataset demonstrate that the proposed approach is promising for recognition under less controlled and thus more challenging environment.