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Face registration is a major and critical step for face analysis. Existing facial activity recognition systems often employ coarse face alignment based on a few fiducial points such as eyes and extract features from equal-sized grid. Such extracted features are susceptible to variations in face pose, facial deformation, and person-specific geometry. In this work, we propose a novel face registration method named facial grid transformation to improve feature extraction for recognizing facial Action Units (AUs). Based on the transformed grid, novel grid edge features are developed to capture local facial motions related to AUs. Extensive experiments on two wellknown AU-coded databases have demonstrated that the proposed method yields significant improvements over the methods based on equal-sized grid on both posed and more importantly, spontaneous facial displays. Furthermore, the proposed method also outperforms the state-of-the-art methods using either coarse alignment or mesh-based face registration. 

Studies in psychology show that not all facial regions are of importance in recognizing facial expressions and different facial regions make different contributions in various facial expressions. Motivated by this, a novel framework, named Feature Disentangling Machine (FDM), is proposed to effectively select active features characterizing facial expressions. More importantly, the FDM aims to disentangle these selected features into non-overlapped groups, in particular, common features that are shared across different expressions and expression-specific features that are discriminative only for a target expression. Specifically, the FDM integrates sparse support vector machine and multi-task learning in a unified framework, where a novel loss function and a set of constraints are formulated to precisely control the sparsity and naturally disentangle active features. Extensive experiments on two well-known facial expression databases have demonstrated that the FDM outperforms the state-of-the-art methods for facial expression analysis. More importantly, the FDM achieves an impressive performance in a cross-database validation, which demonstrates the generalization capability of the selected features. 

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.