Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework

TL;DR

This paper introduces a comprehensive deep learning framework for affect recognition in real-world conditions, leveraging large in-the-wild databases and multi-task learning to improve accuracy across multiple affective tasks.

Contribution

It presents a novel multi-task, holistic deep neural network framework that jointly learns affect recognition tasks on large in-the-wild databases, achieving superior performance.

Findings

Significant performance improvements over state-of-the-art methods.

Effective generalization across multiple affective recognition tasks.

Successful training on large, real-world affective datasets.

Abstract

Affect recognition based on subjects' facial expressions has been a topic of major research in the attempt to generate machines that can understand the way subjects feel, act and react. In the past, due to the unavailability of large amounts of data captured in real-life situations, research has mainly focused on controlled environments. However, recently, social media and platforms have been widely used. Moreover, deep learning has emerged as a means to solve visual analysis and recognition problems. This paper exploits these advances and presents significant contributions for affect analysis and recognition in-the-wild. Affect analysis and recognition can be seen as a dual knowledge generation problem, involving: i) creation of new, large and rich in-the-wild databases and ii) design and training of novel deep neural architectures that are able to analyse affect over these databases and to successfully generalise their performance on other datasets. The paper focuses on large in-the-wild databases, i.e., Aff-Wild and Aff-Wild2 and presents the design of two classes of deep neural networks trained with these databases. The first class refers to uni-task affect recognition, focusing on prediction of the valence and arousal dimensional variables. The second class refers to estimation of all main behavior tasks, i.e. valence-arousal prediction; categorical emotion classification in seven basic facial expressions; facial Action Unit detection. A novel multi-task and holistic framework is presented which is able to jointly learn and effectively generalize and perform affect recognition over all existing in-the-wild databases. Large experimental studies illustrate the achieved performance improvement over the existing state-of-the-art in affect recognition.