@inproceedings{bbf0f8928d6f4778936a07ff798e341e,
title = "Classifying posed and real smiles from observers{\textquoteright} peripheral physiology",
abstract = "Smiles are important signals in face-to-face communication that provides impressions / feelings to observers. For example, a speaker can be motivated from audience smiles. People can smile from feeling or by acting or posing the smile. We used observers' physiological signals such as PR (Pupillary Response), BVP (Blood Volume Pulse), and GSR (Galvanic Skin Response) to classify smilers' real (elicited) and posed (asked to act) smiles. Twenty smile videos were collected from benchmark datasets and shown to 24 observers while asking them to make choices, and recording their physiological signals. A leave-one-video-out process was used to measure classification accuracies, and was 93.7% accurate for PR features.",
keywords = "Affective computing, Classification, Observers, Physiological signals, Posed and real smiles",
author = "Hossain, {Md Zakir}",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2017 ; Conference date: 23-05-2017 Through 26-05-2017",
year = "2017",
doi = "10.1145/3154862.3154893",
language = "English",
isbn = "9781450363631",
series = "PervasiveHealth: Pervasive Computing Technologies for Healthcare",
publisher = "Association for Computing Machinery",
pages = "460--463",
editor = "Nuria Oliver and Mary Czerwinski",
booktitle = "Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, 2017",
}