AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild

TL;DR

AffectNet is the largest publicly available database of facial images in the wild, annotated for both discrete expressions and continuous valence-arousal, enabling advanced research in affective computing.

Contribution

This paper introduces AffectNet, a large-scale, annotated database for facial expressions and valence-arousal, filling a critical gap in affective computing resources.

Findings

Deep neural networks outperform traditional methods in expression recognition

AffectNet enables research in both categorical and dimensional emotion models

Baseline models achieve high accuracy on affective recognition tasks

Abstract

Automated affective computing in the wild setting is a challenging problem in computer vision. Existing annotated databases of facial expressions in the wild are small and mostly cover discrete emotions (aka the categorical model). There are very limited annotated facial databases for affective computing in the continuous dimensional model (e.g., valence and arousal). To meet this need, we collected, annotated, and prepared for public distribution a new database of facial emotions in the wild (called AffectNet). AffectNet contains more than 1,000,000 facial images from the Internet by querying three major search engines using 1250 emotion related keywords in six different languages. About half of the retrieved images were manually annotated for the presence of seven discrete facial expressions and the intensity of valence and arousal. AffectNet is by far the largest database of facial expression, valence, and arousal in the wild enabling research in automated facial expression recognition in two different emotion models. Two baseline deep neural networks are used to classify images in the categorical model and predict the intensity of valence and arousal. Various evaluation metrics show that our deep neural network baselines can perform better than conventional machine learning methods and off-the-shelf facial expression recognition systems.