Deep Evolution for Facial Emotion Recognition

• Main Authors: Dufourq, Emmanuel, Bassett, Bruce A
• Format: Journal Article
• Language: English
• Published: 13-10-2020
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Computer Science - Neural and Evolutionary Computing
• DOI: 10.48550/arxiv.2009.14194

Deep facial expression recognition faces two challenges that both stem from
the large number of trainable parameters: long training times and a lack of
interpretability. We propose a novel method based on evolutionary algorithms,
that deals with both challenges by massively reducing the number of trainable
parameters, whilst simultaneously retaining classification performance, and in
some cases achieving superior performance. We are robustly able to reduce the
number of parameters on average by 95% (e.g. from 2M to 100k parameters) with
no loss in classification accuracy. The algorithm learns to choose small
patches from the image, relative to the nose, which carry the most important
information about emotion, and which coincide with typical human choices of
important features. Our work implements a novel form attention and shows that
evolutionary algorithms are a valuable addition to machine learning in the deep
learning era, both for reducing the number of parameters for facial expression
recognition and for providing interpretable features that can help reduce bias.