Facial expression recognition via joint deep learning of rgb-depth map latent representations
OK Oyedotun, G Demisse… - Proceedings of the …, 2017 - openaccess.thecvf.com
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 …
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 …
[PDF][PDF] Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Representations
D Aouada, B Ottersten - 2017 - researchgate.net
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 …
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 …
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