2020 |
Puc, Andraž; Štruc, Vitomir; Grm, Klemen Analysis of Race and Gender Bias in Deep Age Estimation Model Proceedings Article In: Proceedings of EUSIPCO 2020, 2020. Abstract | Links | BibTeX | Tags: age estimation, bias, bias analysis, biometrics, face analysis @inproceedings{GrmEUSIPCO2020,Due to advances in deep learning and convolutional neural networks (CNNs) there has been significant progress in the field of visual age estimation from face images over recent years. While today's models are able to achieve considerable age estimation accuracy, their behaviour, especially with respect to specific demographic groups is still not well understood. In this paper, we take a deeper look at CNN-based age estimation models and analyze their performance across different race and gender groups. We use two publicly available off-the-shelf age estimation models, i.e., FaceNet and WideResNet, for our study and analyze their performance on the UTKFace and APPA-REAL datasets. We partition face images into sub-groups based on race, gender and combinations of race and gender. We then compare age estimation results and find that there are noticeable differences in performance across demographics. Specifically, our results show that age estimation accuracy is consistently higher for men than for women, while race does not appear to have consistent effects on the tested models across different test datasets. |