Multi-View Exclusive Unsupervised Dimension Reduction for Video-Based Facial Expression Recognition / 2217
Liping Xie, Dacheng Tao, Haikun Wei
Video-based facial expression recognition (FER)has recently received increased attention as a result of its widespread application. Many kinds of features have been proposed to represent different properties of facial expressions in videos. However the dimensionality of these features is usually high. In addition, due to the complexity of the information available in video sequences, using only one type of feature is often inadequate. How to effectively reduce the dimensionality and combine multi-view features thus becomes a challenging problem. In this paper, motivated by the recent success in exclusive feature selection, we first introduce exclusive group LASSO (EG-LASSO) to unsupervised dimension reduction (UDR). This leads to the proposed exclusive UDR (EUDR) framework, which allows arbitrary sparse structures on the feature space. To properly combine multiple kinds of features, we further extend EUDR to multi-view EUDR (MEUDR), where the structured sparsity is enforced at both intra- and inter-view levels. In addition, combination weights are learned for all views to allow them to contribute differently to the final consensus presentation. A reliable solution is then obtained. Experiments on two challenging video-based FER datasets demonstrate the effectiveness of the proposed method.