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
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.