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Face alignment consists of aligning a shape model on a face image. It is anactive domain in computer vision as it is a preprocessing for a number of faceanalysis and synthesis applications. Current state-of-the-art methods alreadyperform well on "easy" datasets, with moderate head pose variations, but maynot be robust for "in-the-wild" data with poses up to 90{\deg}. In order toincrease robustness to an ensemble of factors of variations (e.g. head pose orocclusions), a given layer (e.g. a regressor or an upstream CNN layer) can bereplaced by a Mixture of Experts (MoE) layer that uses an ensemble of expertsinstead of a single one. The weights of this mixture can be learned as gatingfunctions to jointly learn the experts and the corresponding weights. In thispaper, we propose to use tree-structured gates which allows a hierarchicalweighting of the experts (Tree-MoE). We investigate the use of Tree-MoE layersin different contexts in the frame of face alignment with cascaded regression,firstly for emphasizing relevant, more specialized feature extractors dependingof a high-level semantic information such as head pose (Pose-Tree-MoE), andsecondly as an overall more robust regression layer. We perform extensiveexperiments on several challenging face alignment datasets, demonstrating thatour approach outperforms the state-of-the-art methods.

Tree-gated Deep Mixture-of-Experts For Pose-robust Face Alignment