Affective computing
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Boosting encoded dynamic features for facial expression recognition
Pattern Recognition Letters
Latent topic driving model for movie affective scene classification
MM '09 Proceedings of the 17th ACM international conference on Multimedia
SVR-based music mood classification and context-based music recommendation
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Dynamics of facial expression extracted automatically from video
Image and Vision Computing
| Hi-index | 0.00 |
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.