NDLI: Deep learning based FACS Action Unit occurrence and intensity estimation

Content Provider	IEEE Xplore Digital Library
Author	Gudi, A. Tasli, H.E. den Uyl, T.M. Maroulis, A.
Copyright Year	2015
Description	Author affiliation: Vicarious Perception Technol., Amsterdam, Netherlands (Gudi, A.; Tasli, H.E.; den Uyl, T.M.; Maroulis, A.)
Abstract	Ground truth annotation of the occurrence and intensity of FACS Action Unit (AU) activation requires great amount of attention. The efforts towards achieving a common platform for AU evaluation have been addressed in the FG 2015 Facial Expression Recognition and Analysis challenge (FERA 2015). Participants are invited to estimate AU occurrence and intensity on a common benchmark dataset. Conventional approaches towards achieving automated methods are to train multiclass classifiers or to use regression models. In this paper, we propose a novel application of a deep convolutional neural network (CNN) to recognize AUs as part of FERA 2015 challenge. The 7 layer network is composed of 3 convolutional layers and a max-pooling layer. The final fully connected layers provide the classification output. For the selected tasks of the challenge, we have trained two different networks for the two different datasets, where one focuses on the AU occurrences and the other on both occurrences and intensities of the AUs. The occurrence and intensity of AU activation are estimated using specific neuron activations of the output layer. This way, we are able to create a single network architecture that could simultaneously be trained to produce binary and continuous classification output.
Starting Page	1
Ending Page	5
File Size	2469559
Page Count	5
File Format	PDF
ISBN	9781479960262
DOI	10.1109/FG.2015.7284873
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-04
Publisher Place	Slovenia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face Gold Training Conferences Machine learning Estimation Face recognition
Content Type	Text
Resource Type	Article

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