Spatiotemporal Attention Based Deep Neural Networks for Emotion Recognition

Jiyoung Lee, Sunok Kim, Seungryong Kiim, Kwanghoon Sohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1513-1517
Number of pages5
ISBN (Print)9781538646588
DOIs
Publication statusPublished - 2018 Sep 10
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 2018 Apr 152018 Apr 20

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period18/4/1518/4/20

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Lee, J., Kim, S., Kiim, S., & Sohn, K. (2018). Spatiotemporal Attention Based Deep Neural Networks for Emotion Recognition. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (pp. 1513-1517). [8461920] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8461920