Video indexing and recommendation based on affective analysis of viewers

Most previous works on video indexing and recommendation were only based on the content of video itself, without considering the affective analysis of viewers, which is an efficient and important way to reflect viewers' attitudes, feelings and evaluations of videos. In this paper, we propose a novel method to index and recommend videos based on affective analysis, mainly on facial expression recognition of viewers. We first build a facial expression recognition classifier by embedding the process of building compositional Haar-like features into hidden conditional random fields (HCRFs). Then we extract viewers' facial expressions frame by frame through the videos, collected from the camera when viewers are watching videos, to obtain the affections of viewers. Finally, we draw the affective curve which tells the process of affection changes. Through the curve, we segment each video into affective sections, give the indexing result of the videos, and list recommendation points from views' aspect. Experiments on our collected database from the web show that the proposed method has a promising performance.