Achievement

Automated recognition of emotions in video has been challenging. We have developed a method that contributes to the state-of-the-art in two ways: (1) A face alignment algorithm that is powerful enough to align faces despite facial dynamics, such as pose, expression and gesture. (2) A method, inspired by the behavior of the human visual system, for selectively processing frames, allowing real-time processing. Current face alignment algorithms fail in extreme pose, or when the face is occluded. We overcome this by warping each face to a target image in an intelligent manner. State-of-the-art systems process every frame of video. They are not fast enough for real-time applications. We selectively process a video in the same way the human visual system (HVS) processes a scene. The approach was submitted and presented at the Audio/Visual Emotion Challenge 2011. The proposed approach improved state of the art approach by 10%.