One Face, Many Views: Cross-View Consistency of Facial Action Unit Analysis in Multi-Camera Settings

Facial Action Units (AUs) represent individual facial muscle movements and are the building blocks for recognizing expressions and emotions. As such, AU detection is fundamental in facial affect analysis (FAA). While FAA systems are increasingly deployed in real-world applications, most AU detection models are trained and evaluated on frontfacing, well-framed images, which overlook the variability of different camera angles. To address this gap, we introduce MultiFace7, a large-scale, multi-view video corpus designed to evaluate the robustness of AU detection across camera views. Using synchronized recordings, we benchmark open-source and commercial FAA tools by extracting AU features and comparing their consistency across views using a statistical correlation analysis. Our analysis reveals substantial inconsistencies in AU intensity detection depending on the camera angle. These findings highlight limitations in current FAA systems and raise concerns about their trustworthiness in unconstrained environments where optimal camera positioning cannot be guaranteed. While a few view-invariant models and multi-view datasets exist, they are limited in scope, often rely on still images or synthetic views, and lack evidence of use in realworld FAA applications. Our work underscores the need for updated, video-based multi-view benchmarks and more robust, operationally viable AU detection models.