3D dynamic facial expression recognition using low-resolution videos

We develop a 4D facial expression recognition algorithm.Our algorithm is suitable for both high and low-resolution RGB-D videos.4D feature learning is used for facial expression recognition.We demonstrate that feature learning is extremely effective in this setting.Extensive experimental comparisons and discussions are made by the end of the paper. In this paper, we focus on the problem of 3D dynamic (4D) facial expression recognition. While traditional methods rely on building deformation models on high-resolution 3D meshes, our approach works directly on low-resolution RGB-D sequences; this feature allows us to apply our algorithm to videos retrieved by widespread and standard low-resolution RGB-D sensors, such as Kinect. After preprocessing both RGB and depth image sequences, sparse features are learned from spatio-temporal local cuboids. Conditional Random Fields classifier is then employed for training and classification. The proposed system is fully-automatic and achieves superior results on three low-resolution datasets built from the 4D facial expression recognition dataset - BU-4DFE. Extensive evaluations of our approach and comparisons with state-of-the-art methods are presented.