Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55341
Title: Recognition of facial action units with action unit classifiers and an association network
Authors: Chen, J
Chen, Z
Chi, Z 
Fu, H
Issue Date: 2015
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Most previous work of facial action recognition focused only on verifying whether a certain facial action unit appeared or not on a face image. In this paper, we report our investigation on the semantic relationships of facial action units and introduce a novel method for facial action unit recognition based on action unit classifiers and a Bayes network called Facial Action Unit Association Network (FAUAN). Compared with other methods, the proposed method attempts to identify a set of facial action units of a face image simultaneously. We achieve this goal by three steps. At first, the histogram of oriented gradients (HOG) is extracted as features and after that, a Multi-Layer Perceptron (MLP) is trained for the preliminary detection of each individual facial action unit. At last, FAUAN fuses the responses of all the facial action unit classifiers to determine a best set of facial action units. The proposed method achieves a promising performance on the extended Cohn-Kanade Dataset. Experimental results also show that when the individual unit classifiers are not so good, the performance could improve by nearly 10% in some cases when FAUAN is used.
Description: ACCV 2014 Workshops : Singapore, Singapore, November 1-2, 2014
URI: http://hdl.handle.net/10397/55341
ISBN: 9783319166308
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-16631-5_49
Appears in Collections:Conference Paper

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