Action unit classification using active appearance models and conditional random fields.

Cognitive Processing
Laurens van der Maaten, Emile Hendriks

Abstract

In this paper, we investigate to what extent modern computer vision and machine learning techniques can assist social psychology research by automatically recognizing facial expressions. To this end, we develop a system that automatically recognizes the action units defined in the facial action coding system (FACS). The system uses a sophisticated deformable template, which is known as the active appearance model, to model the appearance of faces. The model is used to identify the location of facial feature points, as well as to extract features from the face that are indicative of the action unit states. The detection of the presence of action units is performed by a time series classification model, the linear-chain conditional random field. We evaluate the performance of our system in experiments on a large data set of videos with posed and natural facial expressions. In the experiments, we compare the action units detected by our approach with annotations made by human FACS annotators. Our results show that the agreement between the system and human FACS annotators is higher than 90% and underlines the potential of modern computer vision and machine learning techniques to social psychology research. We conclude with some su...Continue Reading

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Citations

Feb 1, 2017·IEEE Transactions on Neural Networks and Learning Systems·Wenjie PeiLaurens van der Maaten
Feb 10, 2021·The Journal of Immunology : Official Journal of the American Association of Immunologists·Ashley A BrateNitin J Karandikar

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Methods Mentioned

BETA
FACS
to
assign
by
human
PCA

Software Mentioned

FACS

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