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Authors: François Courtemanche 1 ; Emma Campbell 2 ; Pierre-Majorique Léger 1 and Franco Lepore 3

Affiliations: 1 HEC Montréal, Canada ; 2 HEC Montréal and University of Montreal, Canada ; 3 University of Montreal, Canada

ISBN: 978-989-758-085-7

Keyword(s): Affective Signal Processing, Subject-dependency, Psychophysiological Inference, Personality.

Related Ontology Subjects/Areas/Topics: Affective Computing ; Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Biosignal Acquisition, Analysis and Processing ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: Most works on Affective Signal Processing (ASP) focus on user-dependent emotion recognition models which are personalized to a specific subject. As these types of approach have good accuracy rates, they cannot easily be reused with other subjects for industrial or research purposes. On the other hand, the reported accuracy rates of user-independent models are substantially lower. This performance decrease is mostly due to the greater variance in the physiological training data set drawn from multiple users. In this paper, we propose an approach to address this problem and enhance the performance of user-independent models by explicitly modeling subjects’ idiosyncrasies. As a first exemplification, we describe how personality traits can be used to improve the accuracy of user-independent emotion recognition models. We also present the experiment that will be carried on to validate the proposed approach.

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Paper citation in several formats:
Courtemanche, F.; Campbell, E.; Léger, P. and Lepore, F. (2015). Addressing Subject-dependency for Affective Signal Processing - Modeling Subjects’ Idiosyncracies.In Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS, ISBN 978-989-758-085-7, pages 72-77. DOI: 10.5220/0005330700720077

@conference{phycs15,
author={Fran\c{C}ois Courtemanche. and Emma Campbell. and Pierre{-}Majorique Léger. and Franco Lepore.},
title={Addressing Subject-dependency for Affective Signal Processing - Modeling Subjects’ Idiosyncracies},
booktitle={Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS,},
year={2015},
pages={72-77},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005330700720077},
isbn={978-989-758-085-7},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - Addressing Subject-dependency for Affective Signal Processing - Modeling Subjects’ Idiosyncracies
SN - 978-989-758-085-7
AU - Courtemanche, F.
AU - Campbell, E.
AU - Léger, P.
AU - Lepore, F.
PY - 2015
SP - 72
EP - 77
DO - 10.5220/0005330700720077

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