Recognizing Expressions from Face and Body Gesture by Temporal Normalized Motion and Appearance Features

Abstract

Recently, recognizing affects from both face and body gestures attracts more attentions. However, it still lacks of efficient and effective features to describe the dynamics of face and gestures for real-time automatic affect recognition. In this paper, we propose a novel approach, which combines both MHI-HOG and Image-HOG through temporal normalization method, to describe the dynamics of face and body gestures for affect recognition. The MHI-HOG stands for Histogram of Oriented Gradients (HOG) on the Motion History Image (MHI). It captures motion direction of an interest point as an expression evolves over the time. The Image-HOG captures the appearance information of the corresponding interesting point. Combination of MHI-HOG and Image-HOG can effectively represent both local motion and appearance information of face and body gesture for affect recognition. The temporal normalization method explicitly solves the time resolution issue in the video-based affect recognition. Experimental results demonstrate promising performance as compared with the state of the art. We also show that expression recognition with temporal dynamics outperforms frame-based recognition.