Exploring the Facial Expression Perception-Production Link Using Real-Time Automated Facial Expression Recognition

David M. Deriso¹, Josh Susskind¹, Jim Tanaka², Piotr Winkielman³, John Herrington⁴, Robert Schultz⁴, and Marian Bartlett¹

¹Machine Perception Laboratory, University of California, San Diego, USA
dderiso@ucsd.edu
josh@mplab.ucsd.edu
marni@mplab.ucsd.edu

²Department of Psychology, University of Victoria, Canada

³Department of Psychology, University of California, San Diego, USA

⁴Center for Autism Research, Children’s Hospital of Philadelphia, USA

Abstract. Motor production may play an important role in learning to recognize facial expressions. The present study explores the influence of facial production training on the perception of facial expressions by employing a novel production training intervention built on feedback from automated facial expression recognition. We hypothesized that production training using the automated feedback system would improve an individual’s ability to identify dynamic emotional faces. Thirty-four participants were administered a dynamic expression recognition task before and after either interacting with a production training video game called the Emotion Mirror or playing a control video game. Consistent with the prediction that perceptual benefits are tied to expression production, individuals with high engagement in production training improved more than individuals with low engagement or individuals who did not receive production training. These results suggest that the visual-motor associations involved in expression production training are related to perceptual abilities. Additionally, this study demonstrates a novel application of computer vision for real-time facial expression intervention training.

LNCS 7584, p. 270 ff.

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