Content-Based Video Emotion Tagging Augmented by Users' Multiple Physiological Responses

The intrinsic interactions among a video's emotion tag, its content, and a user's spontaneous responses while consuming the video can be leveraged to improve video emotion tagging, but such interactions have not been thoroughly exploited yet. In this paper, we propose a novel content-based video emotion tagging approach augmented by users' multiple physiological responses, which are only required during training. Specifically, a better emotion tagging model is constructed by introducing similarity constraints on the classifiers from video content and multiple physiological signals available during training. Maximum margin classifiers are adopted and efficient learning algorithms of the proposed model are also developed. Furthermore, the proposed video emotion tagging approach is extended to utilize incomplete physiological signals, since these signals are often corrupted by artifacts. Experiments on four benchmark databases demonstrate the effectiveness of the proposed method for implicitly integrating multiple physiological responses, and its superior performance to existing methods using both complete and incomplete multiple physiological signals.