Towards Fair Affective Robotics: Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition

03/15/2021
by   Ozgur Kara, et al.
28

As affective robots become integral in human life, these agents must be able to fairly evaluate human affective expressions without discriminating against specific demographic groups. Identifying bias in Machine Learning (ML) systems as a critical problem, different approaches have been proposed to mitigate such biases in the models both at data and algorithmic levels. In this work, we propose Continual Learning (CL) as an effective strategy to enhance fairness in Facial Expression Recognition (FER) systems, guarding against biases arising from imbalances in data distributions. We compare different state-of-the-art bias mitigation approaches with CL-based strategies for fairness on expression recognition and Action Unit (AU) detection tasks using popular benchmarks for each; RAF-DB and BP4D. Our experiments show that CL-based methods, on average, outperform popular bias mitigation techniques, strengthening the need for further investigation into CL for the development of fairer FER algorithms.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 2

03/15/2021

Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition

As Facial Expression Recognition (FER) systems become integrated into ou...
07/20/2020

Investigating Bias and Fairness in Facial Expression Recognition

Recognition of expressions of emotions and affect from facial images is ...
08/19/2021

Understanding and Mitigating Annotation Bias in Facial Expression Recognition

The performance of a computer vision model depends on the size and quali...
01/05/2022

The Effect of Model Compression on Fairness in Facial Expression Recognition

Deep neural networks have proved hugely successful, achieving human-like...
04/25/2019

A Deeper Look at Facial Expression Dataset Bias

Datasets play an important role in the progress of facial expression rec...
06/10/2020

Continual Learning for Affective Computing

Real-world application require affect perception models to be sensitive ...
06/30/2021

Fair Visual Recognition in Limited Data Regime using Self-Supervision and Self-Distillation

Deep learning models generally learn the biases present in the training ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.