Fundamentals of speech recognition
Fundamentals of speech recognition
Facial Expression Recognition Using a Neural Network
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Disambiguating Visual Motion Through Contextual Feedback Modulation
Neural Computation
A Fast Biologically Inspired Algorithm for Recurrent Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
From facial expression to level of interest: a spatio-temporal approach
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Orientation histograms for face recognition
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Human expression recognition from motion using a radial basis function network architecture
IEEE Transactions on Neural Networks
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Multiple classifier systems for the classificatio of audio-visual emotional states
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Spectral graph features for the classification of graphs and graph sequences
Computational Statistics
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One of the important properties of hidden Markov models is the ability to model sequential dependencies. In this study the applicability of hidden Markov models for emotion recognition in image sequences is investigated, i.e. the temporal aspects of facial expressions. The underlying image sequences were taken from the Cohn-Kanade database. Three different features (principal component analysis, orientation histograms and optical flow estimation) from four facial regions of interest (face, mouth, right and left eye) were extracted. The resulting twelve paired combinations of feature and region were used to evaluate hidden Markov models. The best single model with features of principal component analysis in the region face achieved a detection rate of 76.4 %. To improve these results further, two different fusion approaches were evaluated. Thus, the best fusion detection rate in this study was 86.1 %.