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A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction. Extensive empirical studies are needed to search for an optimal combination of feature representation, feature set, and classifier to achieve good recognition performance. This paper presents a novel Boosted Deep Belief Network (BDBN) for performing the three training stages iteratively in a unified loopy framework. Through the proposed BDBN framework, a set of features, which is effective to characterize expression-related facial appearance/shape changes, can be learned and selected to form a boosted strong classifier in a statistical way. As learning continues, the strong classifier is improved iteratively and more importantly, the discriminative capabilities of selected features are strengthened as well according to their relative importance to the strong classifier via a joint fine-tune process in the BDBN framework. Extensive experiments on two public databases showed that the BDBN framework yielded dramatic improvements in facial expression analysis. 

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.