Virtual EMG via Facial Video Analysis

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.