Domain Generalisation for Apparent Emotional Facial Expression
Recognition across Age-Groups
- URL: http://arxiv.org/abs/2110.09168v1
- Date: Mon, 18 Oct 2021 10:35:40 GMT
- Title: Domain Generalisation for Apparent Emotional Facial Expression
Recognition across Age-Groups
- Authors: Rafael Poyiadzi, Jie Shen, Stavros Petridis, Yujiang Wang, and Maja
Pantic
- Abstract summary: We study the effect of using different age-groups for training apparent emotional facial expression recognition models.
We show that an increase in the number of training age-groups tends to increase the apparent emotional facial expression recognition performance on unseen age-groups.
- Score: 55.56174840049614
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Apparent emotional facial expression recognition has attracted a lot of
research attention recently. However, the majority of approaches ignore age
differences and train a generic model for all ages. In this work, we study the
effect of using different age-groups for training apparent emotional facial
expression recognition models. To this end, we study Domain Generalisation in
the context of apparent emotional facial expression recognition from facial
imagery across different age groups. We first compare several domain
generalisation algorithms on the basis of out-of-domain-generalisation, and
observe that the Class-Conditional Domain-Adversarial Neural Networks (CDANN)
algorithm has the best performance. We then study the effect of variety and
number of age-groups used during training on generalisation to unseen
age-groups and observe that an increase in the number of training age-groups
tends to increase the apparent emotional facial expression recognition
performance on unseen age-groups. We also show that exclusion of an age-group
during training tends to affect more the performance of the neighbouring age
groups.
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