Generative Adversarial Stacked Autoencoders for Facial Pose Normalization and Emotion Recognition

• URL: http://arxiv.org/abs/2007.09790v1
• Date: Sun, 19 Jul 2020 21:47:16 GMT
• Title: Generative Adversarial Stacked Autoencoders for Facial Pose Normalization and Emotion Recognition
• Authors: Ariel Ruiz-Garcia, Vasile Palade, Mark Elshaw, Mariette Awad
• Abstract summary: We propose a Generative Adversarial Stacked Autoencoder that learns to map facial expressions. We report state-of-the-art performance on several facial emotion recognition corpora, including one collected in the wild.
• Score: 4.620526905329234
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this work, we propose a novel Generative Adversarial Stacked Autoencoder that learns to map facial expressions, with up to plus or minus 60 degrees, to an illumination invariant facial representation of 0 degrees. We accomplish this by using a novel convolutional layer that exploits both local and global spatial information, and a convolutional layer with a reduced number of parameters that exploits facial symmetry. Furthermore, we introduce a generative adversarial gradual greedy layer-wise learning algorithm designed to train Adversarial Autoencoders in an efficient and incremental manner. We demonstrate the efficiency of our method and report state-of-the-art performance on several facial emotion recognition corpora, including one collected in the wild.

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