NDLI: Assessing naturalness and emotional intensity: a perceptual study of animated facial motion

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Assessing naturalness and emotional intensity: a perceptual study of animated facial motion

Content Provider	ACM Digital Library
Author	Hyde, Jennifer Kiesler, Sara Carter, Elizabeth J. Hodgins, Jessica K.
Abstract	Animated characters appear in applications for entertainment, education, and therapy. When these characters display appropriate emotions for their context, they can be particularly effective. Characters can display emotions by accurately mimicking the facial expressions and vocal cues that people display or by damping or exaggerating the emotionality of the expressions. In this work, we explored which of these strategies would be most effective for animated characters. We investigated the effects of altering the auditory and facial levels of expressiveness on emotion recognition accuracy and ratings of perceived emotional intensity and naturalness. We ran an experiment with emotion (angry, happy, sad), auditory emotion level (low, high), and facial motion magnitude (damped, unaltered, exaggerated) as within-subjects factors. Participants evaluated animations of a character whose facial motion matched that of an actress we tracked using an active appearance model. This method of tracking and animation can capture subtle facial motions in real-time, a necessity for many interactive animated characters. We manipulated auditory emotion level by asking the actress to speak sentences at varying levels, and we manipulated facial motion magnitude by exaggerating and damping the actress's spatial motion. We found that the magnitude of auditory expressiveness was positively related to emotion recognition accuracy and ratings of emotional intensity. The magnitude of facial motion was positively related to ratings of emotional intensity but negatively related to ratings of naturalness.
Starting Page	15
Ending Page	22
Page Count	8
File Format	PDF
ISBN	9781450330091
DOI	10.1145/2628257.2628267
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Audiovisual perception Animation Emotion Face
Content Type	Text
Resource Type	Article

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