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Head pose estimation and face alignment constitute a backbone preprocessingfor many applications relying on face analysis. While both are closely relatedtasks, they are generally addressed separately, e.g. by deducing the head posefrom the landmark locations. In this paper, we propose to entwine facealignment and head pose tasks inside an attentional cascade. This cascade usesa geometry transfer network for integrating heterogeneous annotations toenhance landmark localization accuracy. Furthermore, we propose adoubly-conditional fusion scheme to select relevant feature maps, and regionsthereof, based on a current head pose and landmark localization estimate. Weempirically show the benefit of entwining head pose and landmark localizationobjectives inside our architecture, and that the proposed AC-DC model enhancesthe state-of-the-art accuracy on multiple databases for both face alignment andhead pose estimation tasks.

Deep Entwined Learning Head Pose and Face Alignment Inside an Attentional Cascade with Doubly-Conditional fusion