Spatiotemporal Attention Based Deep Neural Networks for Emotion Recognition

Jiyoung Lee; Sunok Kim; Seungryong Kim; Kwanghoon Sohn

ICASSP 2018accepted0 citations

Spatiotemporal Attention Based Deep Neural Networks for Emotion Recognition

Jiyoung Lee, Sunok Kim, Seungryong Kim, Kwanghoon Sohn

Abstract

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.

BibTeX

@inproceedings{icassp2018_spatiotemporalat,
  title = {Spatiotemporal Attention Based Deep Neural Networks for Emotion Recognition},
  author = {Jiyoung Lee and Sunok Kim and Seungryong Kim and Kwanghoon Sohn},
  booktitle = {ICASSP 2018},
  year = {2018}
}