Rethinking Affect Analysis: A Protocol for Ensuring Fairness and Consistency
<|reference_start|>Rethinking Affect Analysis: A Protocol for Ensuring Fairness and Consistency: Evaluating affect analysis methods presents challenges due to inconsistencies in database partitioning and evaluation protocols, leading to unfair and biased results. Previous studies claim continuous performance improvements, but our findings challenge such assertions. Using these insights, we propose a unified protocol for database partitioning that ensures fairness and comparability. We provide detailed demographic annotations (in terms of race, gender and age), evaluation metrics, and a common framework for expression recognition, action unit detection and valence-arousal estimation. We also rerun the methods with the new protocol and introduce a new leaderboards to encourage future research in affect recognition with a fairer comparison. Our annotations, code, and pre-trained models are available on \hyperlink{https://github.com/dkollias/Fair-Consistent-Affect-Analysis}{Github}.<|reference_end|>
@article{hu2024rethinking,
    title={Rethinking Affect Analysis: A Protocol for Ensuring Fairness and
  Consistency},
    author={Guanyu Hu and Dimitrios Kollias and Eleni Papadopoulou and Paraskevi
  Tzouveli and Jie Wei and Xinyu Yang},
    journal={arXiv preprint arXiv:2408.02164},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02164},
    primaryClass={cs.CV}
}