IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Fully Automatic Facial Action Unit Detection and Temporal Analysis
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
A review on Gabor wavelets for face recognition
Pattern Analysis & Applications
Toward Practical Smile Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
AVEC 2011-the first international audio/visual emotion challenge
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Contour detection based on nonclassical receptive field inhibition
IEEE Transactions on Image Processing
Independent component analysis of Gabor features for face recognition
IEEE Transactions on Neural Networks
Robust continuous prediction of human emotions using multiscale dynamic cues
Proceedings of the 14th ACM international conference on Multimodal interaction
LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework
Image and Vision Computing
Competitive affective gaming: winning with a smile
Proceedings of the 21st ACM international conference on Multimedia
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Automatic facial expression analysis systems try to build a mapping between the continuous emotion space and a set of discrete expression categories (e.g. happiness, sadness). In this paper, we present a method to recognize emotions in terms of latent dimensions (e.g. arousal, valence, power). The method we applied uses Gabor energy texture descriptors to model the facial appearance deformations, and a multiclass SVM as base learner of emotions. To deal with more naturalistic behavior, the SEMAINE database of naturalistic dialogues was used.