Observer’s galvanic skin response for discriminating real from fake smiles

Md Zakir Hossain, Tom Gedeon, Ramesh Sankaranarayana

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

14 Citations (Scopus)

Abstract

This paper demonstrates a system to discriminate real from fake smiles with high accuracy by sensing observers’ galvanic skin response (GSR). GSR signals are recorded from 10 observers, while they are watching 5 real and 5 posed or acted smile video stimuli. We investigate the effect of various feature selection methods on processed GSR signals (recorded features) and computed features (extracted features) from the processed GSR signals, by measuring classification performance using three different classifiers. A leave-one-observer-out process is implemented to reliably measure classification accuracy. It is found that simple neural network (NN) using random subset feature selection (RSFS) based on extracted features outperforms all other cases, with 96.5% classification accuracy on our two classes of smiles (real vs. fake). The high accuracy highlights the potential of this system for use in the future for discriminating observers’ reactions to authentic emotional stimuli in information systems settings such as advertising and tutoring systems.

Original languageEnglish
Title of host publicationProceedings of the 27th Australasian Conference on Information Systems, ACIS 2016
PublisherUniversity of Wollongong, Faculty of Business
ISBN (Electronic)9781741282672
Publication statusPublished - 2016
Event27th Australasian Conference on Information Systems, ACIS 2016 - Wollongong, Australia
Duration: Dec 5 2016Dec 7 2016

Publication series

NameProceedings of the 27th Australasian Conference on Information Systems, ACIS 2016

Conference

Conference27th Australasian Conference on Information Systems, ACIS 2016
Country/TerritoryAustralia
CityWollongong
Period12/5/1612/7/16

Keywords

  • Extracted features
  • GSR
  • Observers
  • Recorded features
  • Smiles

ASJC Scopus subject areas

  • Information Systems

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