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IJCNN Special Sessions
Oral
Deep and Generative Adversarial Learning

Generative Adversarial Stacked Autoencoders for Facial Pose Normalization and Emotion Recognition

Ariel Ruiz-Garcia

Date & Time

Mon, July 20, 2020

5:45 pm – 7:45 pm

Location

On-Demand

Abstract

In this work, we propose a novel Generative Adversarial Stacked Autoencoder that learns to map facial expressions with up to 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.


Presenter

Ariel Ruiz-Garcia

Coventry University
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Session Chair

Ariel Ruiz-Garcia

Coventry University