Filter by type:

Sort by year:

Enhancing Social Believability of Virtual Agents using Social Power Dynamics

Conference paper
M Hashemian, R Prada, PA Santos, S Mascarenhas

FIDES: How Emotions and Small Talks May Influence Trust in an Embodied vs. Non-embodied Robot

Conference paper
R Paradeda, M Hashemian, C Guerra, R Prada, J Dias, A Paiva

How facial expressions and small talk may influence trust in a robot

RB Paradeda, M Hashemian, RA Rodrigues, A Paiva

“How is his/her mood”: A Question that a Companion Robot may be able to answer

M Hashemian, H Moradi, MS Mirian

Is the Mood Really in the Eye of the Beholder?

M Hashemian, H Moradi, MS Mirian, M Tehrani-Doost, RK Ward

Determining mood via emotions observed in face by induction

Mojgan Hashemian, Hadi Moradi, Maryam S Mirian, Mehdi Tehrani-doost

Determining Mood Using Emotional Features

Conference paper
Mojgan Hashemian, Amin Nikoukaran, Hadi Moradi, Maryam Mirian, Mehdi Tehrani-doost
7th IEEE International Symposium on Telecommunications (pp. 418-423)

1592223

Abstract

The ability to determine mood is one of fundamental challenges in affective computing. In this paper, we present a novel approach for mood detection via emotional variations. In this approach, the mood is considered as a low magnitude and more stable, i.e. low frequency, emotion that can be detected using emotion detection approaches. A Bayes classification is applied on a feature vector composed of statistical aspects of the intensity of the emotions. The approach has been implemented in which two emotions, i.e. happiness and sadness, and also neutral state, have been targeted to determine the good, bad, and neutral, mood of subjects respectively. A Bayes classification is applied on a feature vector containing statistical aspects of the intensity of the emotions. The obtained Correct Classification Rate (CCR) is 91.1, with 0.09 mean error and variance of 4.9 discriminating good mood vs. neutral.