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arXiv · 1805.04136

Unsupervised Deep Representations for Learning Audience Facial Behaviors

Suman Saha·Rajitha Navarathna·Leonhard Helminger·Romann Weber
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Abstract

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.

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Suman Saha, Rajitha Navarathna, Leonhard Helminger, Romann Weber. 2018-05-10. Unsupervised Deep Representations for Learning Audience Facial Behaviors. https://arxiv.org/abs/1805.04136

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