CDRL: Contrastive Disentangled Representation Learning Scheme for Facial Action Unit Detection

Abstract

Facial action unit (AU) detection is a hot topic in computer vision, but it remains challenging due to individual characteristics. Facial action features are a vital informative factor to explain facial anatomical variations but are often entangled with other facial attribution information leading to representation inconsistency within one category. We propose a novel Contrastive Disentangled Representation Autoencoder (CDAE) to learn discriminative identity-invariant representation for AU detection by factorizing face images into temporally varying action parts and stationary facial attributions components. Facial image space is mapped onto the facial action subspace and action-independent identity subspace to disentangle facial action information from identity information. In addition, we design a contrastive learning scheme to obtain a semantic-aware AU manifold by mapping the facial action features onto the continuous space of the latent variables, thus minimizing the misalignment between subjects and reducing the dimension of the facial action features. Experiment results show that CDAE outperforms or is comparable to previous AU detection methods on the challenging BP4D and DISFA benchmarks, demonstrating that the learned facial action representation is discriminative for AU detection.