Spatiotemporal Attention Based Deep Neural Networks for Emotion Recognition

We propose a spatiotemporal attention based deep neural networks for dimensional emotion recognition in facial videos. To learn the spatiotemporal attention that selectively focuses on emotional sailient parts within facial videos, we formulate the spatiotemporal encoder-decoder network using Convol...

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Bibliographic Details
Main Authors: Lee, Jiyoung, Kim, Sunok, Kiim, Seungryong, Sohn, Kwanghoon
Format: Conference Proceeding
Language:English
Subjects:
Convolutional Long Short - Term Memory
Emotion recognition
Face
Face recognition
Feature extraction
Recurrent Neural network
Spatiotemporal attention
Spatiotemporal phenomena
Three-dimensional displays
Videos
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Summary:We propose a spatiotemporal attention based deep neural networks for dimensional emotion recognition in facial videos. To learn the spatiotemporal attention that selectively focuses on emotional sailient parts within facial videos, we formulate the spatiotemporal encoder-decoder network using Convolutional LSTM (ConvLSTM) modules, which can be learned implicitly without any pixel-level annotations. By leveraging the spatiotemporal attention, we also formulate the 3D convolutional neural networks (3D-CNNs) to robustly recognize the dimensional emotion in facial videos. The experimental results show that our method can achieve the state-of-the-art results in dimensional emotion recognition with the highest concordance correlation coefficient (CCC) on RECOLA and AV+EC 2017 dataset.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8461920