  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}.
