@inproceedings{01394600acb24ac89b799ae8397c70d2,
title = "Observer{\textquoteright}s galvanic skin response for discriminating real from fake smiles",
abstract = "This paper demonstrates a system to discriminate real from fake smiles with high accuracy by sensing observers{\textquoteright} 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{\textquoteright} reactions to authentic emotional stimuli in information systems settings such as advertising and tutoring systems.",
keywords = "Extracted features, GSR, Observers, Recorded features, Smiles",
author = "Hossain, {Md Zakir} and Tom Gedeon and Ramesh Sankaranarayana",
note = "Publisher Copyright: {\textcopyright} 2016 Hossain, Gedeon and Sankaranarayana.; 27th Australasian Conference on Information Systems, ACIS 2016 ; Conference date: 05-12-2016 Through 07-12-2016",
year = "2016",
language = "English",
series = "Proceedings of the 27th Australasian Conference on Information Systems, ACIS 2016",
publisher = "University of Wollongong, Faculty of Business",
booktitle = "Proceedings of the 27th Australasian Conference on Information Systems, ACIS 2016",
}