Identifying Real and Posed Smiles from Observers’ Galvanic Skin Response and Blood Volume Pulse

Renshang Gao, Atiqul Islam, Tom Gedeon, Md Zakir Hossain*

*Corresponding author for this work

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

Abstract

This study addresses the question whether galvanic skin response (GSR) and blood volume pulse (BVP) of untrained and unaided observers can be used to identify real and posed smiles from different sets of smile videos or smile images. Observers were shown smile face videos/images, either singly or paired, with the intention to recognise each viewed as real or posed smiles. We created four experimental situations, namely single images (SI), single videos (SV), paired images (PI), and paired videos (PV). The GSR and BVP signals were recorded and processed. Our machine learning classifiers reached the highest accuracy of 93.3%, 87.6%, 92.0%, 91.7% for PV, PI, SV, and SI respectively. Finally, PV and SI were found to be the easiest and hardest way to identify real and posed smiles respectively. Overall, we demonstrated that observers’ subconscious physiological signals (GSR and BVP) are able to identify real and posed smiles at a good accuracy.

Original languageEnglish
Title of host publicationNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
PublisherSpringer Science and Business Media Deutschland GmbH
Pages375-386
Number of pages12
ISBN (Print)9783030638290
DOIs
Publication statusPublished - Jan 1 2020
Event27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
Duration: Nov 18 2020Nov 22 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12532 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Neural Information Processing, ICONIP 2020
CountryThailand
CityBangkok
Period11/18/2011/22/20

Keywords

  • Affective computing
  • Classification
  • Machine learning
  • Physiological signals
  • Smile

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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