Reference : Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Represe...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/32087
Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Representations
English
Oyedotun, Oyebade mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Demisse, Girum mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Shabayek, Abd El Rahman mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Aouada, Djamila mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
21-Aug-2017
2017 IEEE International Conference on Computer Vision Workshop (ICCVW)
Yes
No
International
2017 IEEE International Conference on Computer Vision Workshop (ICCVW)
October 22-29, 2017
Venice
Italy
[en] Facial expression ; recognition ; deep learning
[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.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
This work was funded by the National Research Fund (FNR), Luxembourg, under the project reference R-AGR- 0424-05-D/Bjorn Ottersten
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/32087
FnR ; FNR11295431 > Oyebade Oyedotun > AVR > Automatic Feature Selection For Visual Recognition > 01/02/2017 > 31/01/2021 > 2016

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