In this paper, we present an unsupervised learning approach for analyzing facial behavior based on a deep generative model combined with a convolutional neural network (CNN). We jointly train a variational auto-encoder (VAE) and a generative adversarial network (GAN) to learn a powerful latent representation from footage of audiences viewing feature-length movies. We show that the learned latent representation successfully encodes meaningful signatures of behaviors related to audience engagement (smiling & laughing) and disengagement (yawning). Our results provide a proof of concept for a more general methodology for annotating hard-to-label multimedia data featuring sparse examples of signals of interest.
Most recently, combining Variational Autoencoder (VAEs) and Generative Adversarial Networks (GANs) has allowed for learning a powerful latent representation utilized for facial behavior analysis in audience REF .
Unsupervised Deep Representations for Learning Audience Facial Behaviors
ArXiv
ArXiv
Computer Science