  The field of Automatic Facial Expression Analysis has grown rapidly in recent
years. However, despite progress in new approaches as well as benchmarking
efforts, most evaluations still focus on either posed expressions, near-frontal
recordings, or both. This makes it hard to tell how existing expression
recognition approaches perform under conditions where faces appear in a wide
range of poses (or camera views), displaying ecologically valid expressions.
The main obstacle for assessing this is the availability of suitable data, and
the challenge proposed here addresses this limitation. The FG 2017 Facial
Expression Recognition and Analysis challenge (FERA 2017) extends FERA 2015 to
the estimation of Action Units occurrence and intensity under different camera
views. In this paper we present the third challenge in automatic recognition of
facial expressions, to be held in conjunction with the 12th IEEE conference on
Face and Gesture Recognition, May 2017, in Washington, United States. Two
sub-challenges are defined: the detection of AU occurrence, and the estimation
of AU intensity. In this work we outline the evaluation protocol, the data
used, and the results of a baseline method for both sub-challenges.
