Reference : Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Represe...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/32087

Title :	Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Representations
Language :	English
Author, co-author :	Oyedotun, Oyebade [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Demisse, Girum [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Shabayek, Abd El Rahman [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Aouada, Djamila [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Ottersten, Björn [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Publication date :	21-Aug-2017
Main document title :	2017 IEEE International Conference on Computer Vision Workshop (ICCVW)
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	2017 IEEE International Conference on Computer Vision Workshop (ICCVW)
Event date :	October 22-29, 2017
Event place (city) :	Venice
Event country :	Italy
Keywords :	[en] Facial expression ; recognition ; deep learning
Abstract :	[en] Humans use facial expressions successfully for conveying their emotional states. However, replicating such success in the human-computer interaction domain is an active research problem. In this paper, we propose deep convolutional neural network (DCNN) for joint learning of robust facial expression features from fused RGB and depth map latent representations. We posit that learning jointly from both modalities result in a more robust classifier for facial expression recognition (FER) as opposed to learning from either of the modalities independently. Particularly, we construct a learning pipeline that allows us to learn several hierarchical levels of feature representations and then perform the fusion of RGB and depth map latent representations for joint learning of facial expressions. Our experimental results on the BU-3DFE dataset validate the proposed fusion approach, as a model learned from the joint modalities outperforms models learned from either of the modalities.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Funders :	This work was funded by the National Research Fund (FNR), Luxembourg, under the project reference R-AGR- 0424-05-D/Bjorn Ottersten
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/32087
FnR project :	FnR ; FNR11295431 > Oyebade Oyedotun > AVR > Automatic Feature Selection For Visual Recognition > 01/02/2017 > 31/01/2021 > 2016

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