Virtual EMG via Facial Video Analysis. Boccignone, G., Cuculo, V., Grossi, G., Lanzarotti, R., & Migliaccio, R. In Battiato, S., Gallo, G., Schettini, R., & Stanco, F., editors, Image Analysis and Processing - ICIAP 2017 , pages 197–207, Cham, 2017. Springer International Publishing.
doi  abstract   bibtex   
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.
@InProceedings{BCGLM17,
	author="Boccignone, Giuseppe and Cuculo, Vittorio and Grossi, Giuliano and Lanzarotti, Raffaella and Migliaccio, Raffaella",
	editor="Battiato, Sebastiano and Gallo, Giovanni and Schettini, Raimondo and Stanco, Filippo",
	title="Virtual EMG via Facial Video Analysis",
	booktitle="Image Analysis and Processing - ICIAP 2017          ",
	year="2017",
	publisher="Springer International Publishing",
	address="Cham",
	pages="197--207",
	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.",
	isbn="978-3-319-68560-1",
	doi     ="10.1007/978-3-319-68560-1_18"
}

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