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Impact of Personality on the Recognition of Emotion Expressed via Human, Virtual, and Robotic Embodiments

Pauline Chevalier
Brice Isableu
CMP

Abstract

In this paper, we describe the elaboration and the validation of a body and face database of 96 videos of 1 to 2 seconds of duration, expressing 4 emotions (i.e., anger, happiness, fear, and sadness) elicited through 4 platforms of increased visual complexity and level of embodiment. The final aim of this database is to develop an individualized training program designed for individuals suffering of autism in order to help them recognize various emotions on different test platforms: two robots, a virtual agent, and a human. Before assessing the recognition capabilities of individuals with ASD, we validated our video database on typically developed individuals (TD). Moreover, we also looked at the relationship between the recognition rate and their personality traits (extroverted (EX) vs. introverted (IN)). We found that the personality of our TD participants didn’t lead to a different recognition behavior. However, introverted individuals better recognized emotions from less visually complex characters than extroverted individuals.
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Dates and versions

hal-01203702 , version 1 (23-09-2015)

Identifiers

  • HAL Id : hal-01203702 , version 1

Cite

Pauline Chevalier, Jean-Claude Martin, Brice Isableu, Adriana Tapus. Impact of Personality on the Recognition of Emotion Expressed via Human, Virtual, and Robotic Embodiments. IEEE RO-MAN 2015, Aug 2015, Kobe, Japan. ⟨hal-01203702⟩
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