FER-C: Benchmarking Out-of-Distribution Soft Calibration for Facial
  Expression Recognition
Dexter Neo Tsuhan Chen
We present a soft benchmark for calibrating facial expression recognition
(FER). While prior works have focused on identifying affective states, we find
that FER models are uncalibrated. This is particularly true when
out-of-distribution (OOD) shifts further exacerbate the ambiguity of facial
expressions. While most OOD benchmarks provide hard labels, we argue that the
ground-truth labels for evaluating FER models should be soft in order to better
reflect the ambiguity behind facial behaviours. Our framework proposes soft
labels that closely approximates the average information loss based on
different types of OOD shifts. Finally, we show the benefits of calibration on
five state-of-the-art FER algorithms tested on our benchmark.