Behaviour4All: A Dependency-Aware Toolkit for in-the-wild Facial Behaviour Analysis

Facial behaviour analysis requires understanding valence–arousal (VA), expressions (EXP), action units (AUs), yet existing systems treat these tasks independently or use naïve MTL, ignoring their structured dependencies. This leads to negative transfer, conflicting gradients, poor cross-db generalisation, especially with heterogeneous datasets with only partial labels. We propose Behaviour4All, the first dependency-aware toolkit that unifies VA, EXP, AUs via explicit relational priors and principled optimisation framework. Behaviour4All integrates psychophysically grounded task mappings with coordinated loss families to mitigate gradient conflicts and enforce coherent cross-task semantics. Across eight in-the-wild datasets, Behaviour4All achieves sota performance, improves fairness and generalisation, and outperforms vanilla MTL and student--teacher baselines. We will release the complete toolkit (code, pre-trained models, etc) upon acceptance.