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

TL;DR

This paper introduces the Aff-Wild database and challenge for in-the-wild affect recognition, proposes a deep neural network architecture called AffWildNet, and demonstrates state-of-the-art results in continuous emotion prediction from visual data.

Contribution

It presents the first in-the-wild affect recognition benchmark, organizes a challenge, and develops a novel deep neural network architecture for continuous emotion prediction.

Findings

AffWildNet achieved state-of-the-art results on the Aff-Wild Challenge.

The Aff-Wild database enables training and evaluation of affect recognition models in naturalistic settings.

Features learned from AffWild improve performance on other emotion recognition datasets.

Abstract

Automatic understanding of human affect using visual signals is of great importance in everyday human-machine interactions. Appraising human emotional states, behaviors and reactions displayed in real-world settings, can be accomplished using latent continuous dimensions (e.g., the circumplex model of affect). Valence (i.e., how positive or negative is an emotion) & arousal (i.e., power of the activation of the emotion) constitute popular and effective affect representations. Nevertheless, the majority of collected datasets this far, although containing naturalistic emotional states, have been captured in highly controlled recording conditions. In this paper, we introduce the Aff-Wild benchmark for training and evaluating affect recognition algorithms. We also report on the results of the First Affect-in-the-wild Challenge that was organized in conjunction with CVPR 2017 on the Aff-Wild database and was the first ever challenge on the estimation of valence and arousal in-the-wild. Furthermore, we design and extensively train an end-to-end deep neural architecture which performs prediction of continuous emotion dimensions based on visual cues. The proposed deep learning architecture, AffWildNet, includes convolutional & recurrent neural network layers, exploiting the invariant properties of convolutional features, while also modeling temporal dynamics that arise in human behavior via the recurrent layers. The AffWildNet produced state-of-the-art results on the Aff-Wild Challenge. We then exploit the AffWild database for learning features, which can be used as priors for achieving best performances both for dimensional, as well as categorical emotion recognition, using the RECOLA, AFEW-VA and EmotiW datasets, compared to all other methods designed for the same goal. The database and emotion recognition models are available at http://ibug.doc.ic.ac.uk/resources/first-affect-wild-challenge.