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Human emotions analysis has been the focus of many studies, especially in thefield of Affective Computing, and is important for many applications, e.g.human-computer intelligent interaction, stress analysis, interactive games,animations, etc. Solutions for automatic emotion analysis have also benefitedfrom the development of deep learning approaches and the availability of vastamount of visual facial data on the internet. This paper proposes a novelmethod for human emotion recognition from a single RGB image. We construct alarge-scale dataset of facial videos (\textbf{FaceVid}), rich in facialdynamics, identities, expressions, appearance and 3D pose variations. We usethis dataset to train a deep Convolutional Neural Network for estimatingexpression parameters of a 3D Morphable Model and combine it with an effectiveback-end emotion classifier. Our proposed framework runs at 50 frames persecond and is capable of robustly estimating parameters of 3D expressionvariation and accurately recognizing facial expressions from in-the-wildimages. We present extensive experimental evaluation that shows that theproposed method outperforms the compared techniques in estimating the 3Dexpression parameters and achieves state-of-the-art performance in recognisingthe basic emotions from facial images, as well as recognising stress fromfacial videos. %compared to the current state of the art in emotion recognitionfrom facial images.

Real-time Facial Expression Recognition "In The Wild'' by Disentangling 3D Expression from Identity