FAIR-FER: A Latent Alignment Approach for Mitigating Bias in Facial Expression Recognition (Student Abstract)
Abstract
Facial Expression Recognition (FER) is an extensively explored research problem in the domain of computer vision and artificial intelligence. FER, a supervised learning problem, requires significant training data representative of multiple socio-cultural demographic attributes. However, most of the FER dataset consists of images annotated by humans, which propagates individual and demographic biases. This work attempts to mitigate this bias using representation learning based on latent spaces, thereby increasing a deep learning model's fairness and overall accuracy.
Cite
Text
Rizvi et al. "FAIR-FER: A Latent Alignment Approach for Mitigating Bias in Facial Expression Recognition (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30503Markdown
[Rizvi et al. "FAIR-FER: A Latent Alignment Approach for Mitigating Bias in Facial Expression Recognition (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/rizvi2024aaai-fair/) doi:10.1609/AAAI.V38I21.30503BibTeX
@inproceedings{rizvi2024aaai-fair,
title = {{FAIR-FER: A Latent Alignment Approach for Mitigating Bias in Facial Expression Recognition (Student Abstract)}},
author = {Rizvi, Syed Sameen Ahmad and Seth, Aryan and Narang, Pratik},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23633-23634},
doi = {10.1609/AAAI.V38I21.30503},
url = {https://mlanthology.org/aaai/2024/rizvi2024aaai-fair/}
}