DeepEmo: Real-world facial expression analysis via deep learning

Abstract

Recent automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled pose and lighting conditions, and has produced nearly perfect accuracy. This paper explores the necessary characteristics of the training dataset, feature representations and machine learning algorithms for a system that operates reliably in more realistic conditions. A new database, Real-world Affective Face Database (RAF-DB), is presented which contains about 30,000 greatly-diverse facial images from social networks. Crowdsourcing results suggest that real-world expression recognition problem is a typical imbalanced multi-label classification problem, and the balanced, single-label datasets currently used in the literature could potentially lead research into misleading algorithmic solutions. A deep learning architecture, DeepEmo, is proposed to address the real-world challenge of emotion recognition by learning the highlevel feature representations which are highly effective for discriminating realistic facial expressions. Extensive experimental results show that the deep learning method is significantly superior to handcrafted features, and with the near-frontal pose constraint, human-level recognition accuracy is achievable.