C4.5: programs for machine learning
C4.5: programs for machine learning
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Affective multimodal human-computer interaction
Proceedings of the 13th annual ACM international conference on Multimedia
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Audio-Visual Classification and Fusion of Spontaneous Affective Data in Likelihood Space
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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
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
Correlated-spaces regression for learning continuous emotion dimensions
Proceedings of the 21st ACM international conference on Multimedia
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During face-to-face communication, people continuously exchange para-linguistic information such as their emotional state through facial expressions, posture shifts, gaze patterns and prosody. These affective signals are subtle and complex. In this paper, we propose to explicitly model the interaction between the high level perceptual features using Latent-Dynamic Conditional Random Fields. This approach has the advantage of explicitly learning the sub-structure of the affective signals as well as the extrinsic dynamic between emotional labels. We evaluate our approach on the Audio-Visual Emotion Challenge (AVEC 2011) dataset. By using visual features easily computable using off-the-helf sensing software (vertical and horizontal eye gaze, head tilt and smile intensity), we show that our approach based on LDCRF model outperforms previously published baselines for all four affective dimensions. By integrating audio features, our approach also outperforms the audio-visual baseline.