Active shape models—their training and application
Computer Vision and Image Understanding
Affective computing
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Affective content detection using HMMs
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Active and Dynamic Information Fusion for Facial Expression Understanding from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Haar Features for FACS AU Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure-sensitive manifold ranking for video concept detection
Proceedings of the 15th international conference on Multimedia
Hierarchical movie affective content analysis based on arousal and valence features
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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
Exploiting facial expressions for affective video summarisation
Proceedings of the ACM International Conference on Image and Video Retrieval
Correlative linear neighborhood propagation for video annotation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multimedia Tools and Applications
Affective video content representation and modeling
IEEE Transactions on Multimedia
Video Annotation Based on Kernel Linear Neighborhood Propagation
IEEE Transactions on Multimedia
Affective Level Video Segmentation by Utilizing the Pleasure-Arousal-Dominance Information
IEEE Transactions on Multimedia
Affective Visualization and Retrieval for Music Video
IEEE Transactions on Multimedia
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Most previous works on video classification and recommendation were only based on video contents, without considering the affective analysis of viewers. In this paper, we presented a novel method to classify and recommend videos based on affective analysis, mainly on facial expression recognition of viewers, by fusing the spatio-temporal features. For spatial features, we integrate Haar-like features into compositional ones according to the features' correlation, and train a mid classifier. Then this process is embedded into the improved AdaBoost learning algorithm to obtain spatial features. And for temporal feature fusion, we adopt HDCRFs based on HCRFs by introducing a time dimension variable. The spatial features are embedded into HDCRFs to recognize facial expressions. Experiments on the Cohn-Kanada database show that the proposed method has a promising performance. Then viewers' changing facial expressions are collected frame by frame from the camera when they are watching videos. Finally, we draw affective curves which tell the process of affection changes. Through the curves, we segment each video into affective sections, classify videos into categories, and list recommendation scores. Experimental results on our collected database show that most subjects are satisfied with the classification and recommendation results.