LibreFace 2.0: A Generalizable Facial Expression Analysis Toolkit Leveraging Synthetic Data

Facial expression analysis is central to social AI and human-computer interaction. However, existing toolkits often struggle to generalize across diverse demographics, largely due to the limited diversity of training data for tasks such as action unit (AU) detection, which typically require costly per-frame annotations. In this work, we introduce LibreFace 2.0, a toolkit that leverages recent advances in face generation and motion retargeting to enrich AU datasets with broader demographic coverage. Specifically, we employ stable diffusion to synthesize a wide range of identities spanning age, gender, race and facial attributes and retarget AU motions from annotated datasets onto these generated identities. Training on this large-scale, demographically diverse dataset yields consistent improvements in benchmark performance and enhances fairness across demographic groups. Beyond AU detection and intensity estimation, LibreFace 2.0 also supports facial expression recognition and gaze estimation through lightweight models that achieve competitive accuracy with substantially fewer parameters, enabling efficient inference. Our work provides a scalable approach to achieving fairer face analysis in real-world applications. The code and the synthetic data will be released publicly at https://github.com/ihp-lab/LibreFace.