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The University of Passau Open Emotion Recognition System for the Multimodal Emotion Challenge

  • Jun DengEmail author
  • Nicholas Cummins
  • Jing Han
  • Xinzhou Xu
  • Zhao Ren
  • Vedhas Pandit
  • Zixing Zhang
  • Björn Schuller
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 663)

Abstract

This paper presents the University of Passau’s approaches for the Multimodal Emotion Recognition Challenge 2016. For audio signals, we exploit Bag-of-Audio-Words techniques combining Extreme Learning Machines and Hierarchical Extreme Learning Machines. For video signals, we use not only the information from the cropped face of a video frame, but also the broader contextual information from the entire frame. This information is extracted via two Convolutional Neural Networks pre-trained for face detection and object classification. Moreover, we extract facial action units, which reflect facial muscle movements and are known to be important for emotion recognition. Long Short-Term Memory Recurrent Neural Networks are deployed to exploit temporal information in the video representation. Average late fusion of audio and video systems is applied to make prediction for multimodal emotion recognition. Experimental results on the challenge database demonstrate the effectiveness of our proposed systems when compared to the baseline.

Keywords

Multimodal emotion recognition Bag-of-audio-words Transfer learning Long short-term memory Convolutional neural networks 

Notes

Acknowledgments

This work has been partially supported by the BMBF IKT2020-Grant under grant agreement No. 16SV7213 (EmotAsS), the European Communitys Seventh Framework Programme through the ERC Starting Grant No. 338164 (iHEARu), and the EU’s Horizon 2020 Programme agreement No. 688835 (DE-ENIGMA), and the European Union’s Horizon 2020 Programme through the Innovative Action No. 645094 (SEWA). It was further partially supported by research grants from the China Scholarship Council (CSC) awarded to Xinzhou Xu.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  • Jun Deng
    • 1
    Email author
  • Nicholas Cummins
    • 1
  • Jing Han
    • 1
  • Xinzhou Xu
    • 1
    • 2
  • Zhao Ren
    • 3
  • Vedhas Pandit
    • 1
  • Zixing Zhang
    • 1
  • Björn Schuller
    • 1
  1. 1.Chair of Complex and Intelligent SystemsUniversity of PassauPassauGermany
  2. 2.Technische Universität MünchenMunichGermany
  3. 3.Northwestern Polytechnical UniversityXi’anPeople’s Republic of China

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