In this note, we address the problem of simulating electromyographic signals arising from muscles involved in facial expressions - markedly those conveying affective information -, by relying solely on facial landmarks detected on video sequences. We propose a method that uses the framework of Gaussian Process regression to predict the facial electromyographic signal from videos where people display non-posed affective expressions. To such end, experiments have been conducted on the OPEN EmoRec II multimodal corpus.

Virtual EMG via Facial Video Analysis / G. Boccignone, V. Cuculo, G. Grossi, R. Lanzarotti, R. Migliaccio (LECTURE NOTES IN COMPUTER SCIENCE). - In: Image Analysis and Processing : ICIAP 2017 / [a cura di] S. Battiato, G. Gallo, R. Schettini, F. Stanco. - [s.l] : Springer, 2017. - ISBN 9783319685595. - pp. 197-207 (( Intervento presentato al 19. convegno ICIAP International Conference on Image Analysis and Processing : September, 11-15 tenutosi a Catania nel 2017 [10.1007/978-3-319-68560-1_18].

Virtual EMG via Facial Video Analysis

G. Boccignone
Primo
;
V. Cuculo
Secondo
;
G. Grossi;R. Lanzarotti
Penultimo
;
R. Migliaccio
Ultimo
2017

Abstract

In this note, we address the problem of simulating electromyographic signals arising from muscles involved in facial expressions - markedly those conveying affective information -, by relying solely on facial landmarks detected on video sequences. We propose a method that uses the framework of Gaussian Process regression to predict the facial electromyographic signal from videos where people display non-posed affective expressions. To such end, experiments have been conducted on the OPEN EmoRec II multimodal corpus.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/526585
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