Crossing Domains for AU Coding: Perspectives, Approaches, and Measures

Abstract

Facial action unit (AU) detectors have performed well when trained and tested within the same domain. How well do AU detectors transfer to domains in which they have not been trained? We review literature on cross-domain transfer and conduct experiments to address limitations of prior research. We evaluate generalizability in four publicly available databases. EB+ (an expanded version of BP4D+), Sayette GFT, DISFA and UNBC Shoulder Pain (SP). The databases differ in observational scenarios, context, participant diversity, range of head pose, video resolution, and AU base rates. In most cases performance decreased with change in domain, often to below the threshold needed for behavioral research. However, exceptions were noted. Deep and shallow approaches generally performed similarly and average results were slightly better for deep model compared to shallow one. Occlusion sensitivity maps revealed that local specificity was greater for AU detection within than cross domains. The findings suggest that more varied domains and deep learning approaches may be better suited for generalizability and suggest the need for more attention to characteristics that vary between domains. Until further improvement is realized, caution is warranted when applying AU classifiers from one domain to another.