  Facial pain expression is an important modality for assessing pain,
especially when the patient's verbal ability to communicate is impaired. The
facial muscle-based action units (AUs), which are defined by the Facial Action
Coding System (FACS), have been widely studied and are highly reliable as a
method for detecting facial expressions (FE) including valid detection of pain.
Unfortunately, FACS coding by humans is a very time-consuming task that makes
its clinical use prohibitive. Significant progress on automated facial
expression recognition (AFER) has led to its numerous successful applications
in FACS-based affective computing problems. However, only a handful of studies
have been reported on automated pain detection (APD), and its application in
clinical settings is still far from a reality. In this paper, we review the
progress in research that has contributed to automated pain detection, with
focus on 1) the framework-level similarity between spontaneous AFER and APD
problems; 2) the evolution of system design including the recent development of
deep learning methods; 3) the strategies and considerations in developing a
FACS-based pain detection framework from existing research; and 4) introduction
of the most relevant databases that are available for AFER and APD studies. We
attempt to present key considerations in extending a general AFER framework to
an APD framework in clinical settings. In addition, the performance metrics are
also highlighted in evaluating an AFER or an APD system.
