Fundamentals of speech recognition
Fundamentals of speech recognition
Affective computing
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
IEEE Transactions on Affective Computing
A psychologically-inspired match-score fusion mode for video-based facial expression recognition
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
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
Toward Pose-Invariant 2-D Face Recognition Through Point Distribution Models and Facial Symmetry
IEEE Transactions on Information Forensics and Security - Part 1
AVEC 2012: the continuous audio/visual emotion challenge
Proceedings of the 14th ACM international conference on Multimodal interaction
Proceedings of the 14th ACM international conference on Multimodal interaction
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
AVEC 2013: the continuous audio/visual emotion and depression recognition challenge
Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge
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Predicting human emotions is catching the attention of many research areas, which demand accurate predictions in uncontrolled scenarios. Despite this attractiveness, designed systems for emotion detection are far off being as accurate as desired. Two of the typical measurements in human emotions are described in terms of the dimensions valence and arousal, which shape the Russell's circumplex where complex emotions lie. Thus, the Affect Recognition Sub-Challenge (ASC) of the third AudioVisual Emotion and Depression Challenge, AVEC'13, is focused on estimating these two dimensions. This paper presents a three-level fusion system combining single regression results from audio and visual features, in order to maximize the mean average correlation on both dimensions. Five sets of features are extracted (three for audio and two for video), and they are merged following an iterative process. Results show how this fusion outperforms the baseline method for the challenge database.