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 ![]() | |
Demisse, Girum ![]() | |
Shabayek, Abd El Rahman ![]() | |
Aouada, Djamila ![]() | |
Ottersten, Björn ![]() | |
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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