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Automatic understanding of human affect using visual signals is of greatimportance in everyday human-machine interactions. Appraising human emotionalstates, behaviors and reactions displayed in real-world settings, can beaccomplished using latent continuous dimensions (e.g., the circumplex model ofaffect). Valence (i.e., how positive or negative is an emotion) & arousal(i.e., power of the activation of the emotion) constitute popular and effectiveaffect representations. Nevertheless, the majority of collected datasets thisfar, although containing naturalistic emotional states, have been captured inhighly controlled recording conditions. In this paper, we introduce theAff-Wild benchmark for training and evaluating affect recognition algorithms.We also report on the results of the First Affect-in-the-wild Challenge thatwas organized in conjunction with CVPR 2017 on the Aff-Wild database and wasthe first ever challenge on the estimation of valence and arousal in-the-wild.Furthermore, we design and extensively train an end-to-end deep neuralarchitecture which performs prediction of continuous emotion dimensions basedon visual cues. The proposed deep learning architecture, AffWildNet, includesconvolutional & recurrent neural network layers, exploiting the invariantproperties of convolutional features, while also modeling temporal dynamicsthat arise in human behavior via the recurrent layers. The AffWildNet producedstate-of-the-art results on the Aff-Wild Challenge. We then exploit the AffWilddatabase for learning features, which can be used as priors for achieving bestperformances both for dimensional, as well as categorical emotion recognition,using the RECOLA, AFEW-VA and EmotiW datasets, compared to all other methodsdesigned for the same goal. The database and emotion recognition models areavailable at http://ibug.doc.ic.ac.uk/resources/first-affect-wild-challenge.

Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond