  Recent studies have found that pain in infancy has a significant impact on
infant development, including psychological problems, possible brain injury,
and pain sensitivity in adulthood. However, due to the lack of specialists and
the fact that infants are unable to express verbally their experience of pain,
it is difficult to assess infant pain. Most existing infant pain assessment
systems directly apply adult methods to infants ignoring the differences
between infant expressions and adult expressions. Meanwhile, as the study of
facial action coding system continues to advance, the use of action units (AUs)
opens up new possibilities for expression recognition and pain assessment. In
this paper, a novel AuE-IPA method is proposed for assessing infant pain by
leveraging different engagement levels of AUs. First, different engagement
levels of AUs in infant pain are revealed, by analyzing the class activation
map of an end-to-end pain assessment model. The intensities of top-engaged AUs
are then used in a regression model for achieving automatic infant pain
assessment. The model proposed is trained and experimented on YouTube
Immunization dataset, YouTube Blood Test dataset, and iCOPEVid dataset. The
experimental results show that our AuE-IPA method is more applicable to infants
and possesses stronger generalization ability than end-to-end assessment model
and the classic PSPI metric.
