Supervisor
Dr Muhammad Iqbal
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
This study examines racial bias mitigation in Convolutional Neural Networks (CNNs) for demographic face classification using the FairFace dataset. Three architectures—ResNet50, VGG19, and InceptionV3—are evaluated, with dataset balancing strategies including undersampling and class weighting. Results indicate that InceptionV3 with class weighting achieves the most consistent performance across racial groups, with improved F1-scores and generalization through hyperparameter optimization and data augmentation. Challenges remain in distinguishing visually similar groups, highlighting the need for equitable datasets and fairness-aware training. These insights are critical for ensuring accuracy and fairness in applications such as law enforcement, healthcare, and human–computer interaction.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Andrade, L.
(2025) Improving Fairness in Convolutional Neural Networks for Demographic Face Classification. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.108