Emotion-Aroused Human Behaviors Perception Using RNNPB

Jie Li, Chenguang Yang, Junpei Zhong, Shilu Dai

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

This paper proposes a novel framework to recognize emotions using the algorithm of a recurrent neural network with parameter bias (RNNPB). For this purpose, we performed three simulation experiments aim to explore the relationship between the perception and action. Three types of emotion-driven sequences are fed into the network for training. The training of RNNPB utilizes back-propagation through time (BPTT) method and the parametric bias unit (PB unit) updates in a self-organizing way. The results of the experiments show that the merged sequences can distinguish the emotion better compared to the other two kinds of information.

Original languageEnglish
Title of host publication10th International Conference on Modelling, Identification and Control, ICMIC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538654163
DOIs
Publication statusPublished - 9 Nov 2018
Externally publishedYes
Event10th International Conference on Modelling, Identification and Control, ICMIC 2018 - Guiyang, China
Duration: 2 Jul 20184 Jul 2018

Publication series

Name10th International Conference on Modelling, Identification and Control, ICMIC 2018

Conference

Conference10th International Conference on Modelling, Identification and Control, ICMIC 2018
Country/TerritoryChina
CityGuiyang
Period2/07/184/07/18

Keywords

  • BPTT
  • Emotion Recognition
  • RNNPB

ASJC Scopus subject areas

  • Modelling and Simulation
  • Hardware and Architecture
  • Mechanical Engineering
  • Computational Mathematics
  • Control and Optimization

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