  Action recognition in still images has seen major improvement in recent years
due to advances in human pose estimation, object recognition and stronger
feature representations. However, there are still many cases in which
performance remains far from that of humans. In this paper, we approach the
problem by learning explicitly, and then integrating three components of
transitive actions: (1) the human body part relevant to the action (2) the
object being acted upon and (3) the specific form of interaction between the
person and the object. The process uses class-specific features and relations
not used in the past for action recognition and which use inherently two cycles
in the process unlike most standard approaches. We focus on face-related
actions (FRA), a subset of actions that includes several currently challenging
categories. We present an average relative improvement of 52% over state-of-the
art. We also make a new benchmark publicly available.
