Rethinking Affect Analysis: A Protocol for Ensuring Fairness and Consistency

Understand

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.