NDLI: A multi-label convolutional neural network approach to cross-domain action unit detection

Content Provider	IEEE Xplore Digital Library
Author	Ghosh, S. Laksana, E. Scherer, S. Morency, L.-P.
Copyright Year	2015
Description	Author affiliation: Inst. for Creative Technol., Univ. of Southern California, Los Angeles, CA, USA (Ghosh, S.; Laksana, E.; Scherer, S.) \|\| Language Technol. Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA (Morency, L.-P.)
Abstract	Action Unit (AU) detection from facial images is an important classification task in affective computing. However most existing approaches use carefully engineered feature extractors along with off-the-shelf classifiers. There has also been less focus on how well classifiers generalize when tested on different datasets. In our paper, we propose a multi-label convolutional neural network approach to learn a shared representation between multiple AUs directly from the input image. Experiments on three AU datasets- CK+, DISFA and BP4D indicate that our approach obtains competitive results on all datasets. Cross-dataset experiments also indicate that the network generalizes well to other datasets, even when under different training and testing conditions.
Starting Page	609
Ending Page	615
File Size	1234602
Page Count	7
File Format	PDF
ISSN	21568111
e-ISBN	9781479999538
DOI	10.1109/ACII.2015.7344632
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Cross-dataset transfer Gold Face recognition Action Units Neural networks Feature extraction Convolutional Neural Networks Videos Testing
Content Type	Text
Resource Type	Article

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