Print Email Facebook Twitter Action unit classification using active appearance models and conditional random fields Title Action unit classification using active appearance models and conditional random fields Author Van der Maaten, L.J.P. Hendriks, E.A. Faculty Electrical Engineering, Mathematics and Computer Science Department Mediamatics Date 2011-10-12 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 suggestions on how systems like ours can play an important role in research on social signals. Subject facial expressionsfacial action coding systemactive appearance modelsconditional random fields To reference this document use: http://resolver.tudelft.nl/uuid:2d9e9d94-2339-42ba-8dbe-a7a8e44c217b DOI https://doi.org/10.1007/s10339-011-0419-7 Publisher Springer ISSN 1612-4782 Source http://www.springerlink.com/content/4176k742167u6255/ Source Cognitive Processing, 13 (Suppl 2), 2012 Part of collection Institutional Repository Document type journal article Rights (c) 2011 The Author(s)This article is published with open access at Springerlink.com Files PDF vanderMaaten.pdf 649.62 KB Close viewer /islandora/object/uuid:2d9e9d94-2339-42ba-8dbe-a7a8e44c217b/datastream/OBJ/view