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

Renshang Gao; Atiqul Islam; Tom Gedeon; Md Zakir Hossain

doi:10.1007/978-3-030-63830-6_32

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 proceeding › Conference contribution

5 Citations (Scopus)

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 language	English
Title of host publication	Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
Editors	Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	375-386
Number of pages	12
ISBN (Print)	9783030638290
DOIs	https://doi.org/10.1007/978-3-030-63830-6_32
Publication status	Published - 2020
Externally published	Yes
Event	27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand Duration: Nov 18 2020 → Nov 22 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12532 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th International Conference on Neural Information Processing, ICONIP 2020
Country/Territory	Thailand
City	Bangkok
Period	11/18/20 → 11/22/20

Keywords

Affective computing
Classification
Machine learning
Physiological signals
Smile

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-63830-6_32

Cite this

Gao, R., Islam, A., Gedeon, T., & Hossain, M. Z. (2020). Identifying Real and Posed Smiles from Observers’ Galvanic Skin Response and Blood Volume Pulse. In H. Yang, K. Pasupa, A. C.-S. Leung, J. T. Kwok, J. H. Chan, & I. King (Eds.), Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings (pp. 375-386). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12532 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63830-6_32

Identifying Real and Posed Smiles from Observers’ Galvanic Skin Response and Blood Volume Pulse. / Gao, Renshang; Islam, Atiqul; Gedeon, Tom et al.
Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings. ed. / Haiqin Yang; Kitsuchart Pasupa; Andrew Chi-Sing Leung; James T. Kwok; Jonathan H. Chan; Irwin King. Springer Science and Business Media Deutschland GmbH, 2020. p. 375-386 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12532 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Gao, R, Islam, A, Gedeon, T & Hossain, MZ 2020, Identifying Real and Posed Smiles from Observers’ Galvanic Skin Response and Blood Volume Pulse. in H Yang, K Pasupa, AC-S Leung, JT Kwok, JH Chan & I King (eds), Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12532 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 375-386, 27th International Conference on Neural Information Processing, ICONIP 2020, Bangkok, Thailand, 11/18/20. https://doi.org/10.1007/978-3-030-63830-6_32

Gao R, Islam A, Gedeon T, Hossain MZ. Identifying Real and Posed Smiles from Observers’ Galvanic Skin Response and Blood Volume Pulse. In Yang H, Pasupa K, Leung ACS, Kwok JT, Chan JH, King I, editors, Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 375-386. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-63830-6_32

Gao, Renshang ; Islam, Atiqul ; Gedeon, Tom et al. / Identifying Real and Posed Smiles from Observers’ Galvanic Skin Response and Blood Volume Pulse. Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings. editor / Haiqin Yang ; Kitsuchart Pasupa ; Andrew Chi-Sing Leung ; James T. Kwok ; Jonathan H. Chan ; Irwin King. Springer Science and Business Media Deutschland GmbH, 2020. pp. 375-386 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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{\textquoteright} subconscious physiological signals (GSR and BVP) are able to identify real and posed smiles at a good accuracy.",

keywords = "Affective computing, Classification, Machine learning, Physiological signals, Smile",

author = "Renshang Gao and Atiqul Islam and Tom Gedeon and Hossain, {Md Zakir}",

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AB - 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.

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

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