Version 1
: Received: 25 March 2024 / Approved: 25 March 2024 / Online: 26 March 2024 (08:15:07 CET)
Version 2
: Received: 23 June 2024 / Approved: 24 June 2024 / Online: 24 June 2024 (13:51:16 CEST)
How to cite:
Panić, N.; Marjanović, M.; Bezdan, T. Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models. Preprints2024, 2024031497. https://doi.org/10.20944/preprints202403.1497.v2
Panić, N.; Marjanović, M.; Bezdan, T. Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models. Preprints 2024, 2024031497. https://doi.org/10.20944/preprints202403.1497.v2
Panić, N.; Marjanović, M.; Bezdan, T. Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models. Preprints2024, 2024031497. https://doi.org/10.20944/preprints202403.1497.v2
APA Style
Panić, N., Marjanović, M., & Bezdan, T. (2024). Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models. Preprints. https://doi.org/10.20944/preprints202403.1497.v2
Chicago/Turabian Style
Panić, N., Marina Marjanović and Timea Bezdan. 2024 "Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models" Preprints. https://doi.org/10.20944/preprints202403.1497.v2
Abstract
In this study, we investigate how dataset composition influences the performance and bias of age estimation models across different ethnic groups. We utilized pre-trained Convolutional Neural Networks (CNNs) such as VGG19, fine-tuning them on two datasets: UTKFace and APPA-REAL. UTKFace comprises 23,705 samples (12,391 males, 11,314 females) including 10,078 White, 4,526 Black, and 3,434 Asian individuals. APPA-REAL consists of 7,591 samples (3,818 males, 3,773 females) with 6,686 White, 231 Black, and 674 Asian individuals. We adjusted dataset composition by oversampling minority groups and reducing samples from the majority group to evaluate effects on model performance, measured using Mean Absolute Error (MAE) and standard deviation. Our findings demonstrate that oversampling alone does not guarantee equitable performance across ethnicities; reducing samples from the majority group often resulted in more balanced performance, evidenced by lower MAE standard deviations. These results underscore the importance of a nuanced approach to achieve fairness, including combining various sampling techniques. This study emphasizes the critical need for tailored dataset compositions to mitigate bias and suggests exploring more refined data processing methods and algorithmic adjustments for enhanced model fairness and accuracy in facial recognition technologies.
Keywords
Facial recognition; Age prediction; Convolutional Neural Network (CNN); Ethnicity bias
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
