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

FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge