Tree-gated Deep Regressor Ensemble For Face Alignment In The Wild
This work addresses face alignment for applications like facial recognition and animation, but it is incremental as it builds on existing ensemble methods with a novel weighting approach.
The paper tackles the problem of robust face alignment in unconstrained environments by using an ensemble of deep regressors with a tree-structured adaptive weighting scheme, achieving state-of-the-art performance on challenging datasets.
Face alignment consists in aligning a shape model on a face in an image. It is an active domain in computer vision as it is a preprocessing for applications like facial expression recognition, face recognition and tracking, face animation, etc. Current state-of-the-art methods already perform well on "easy" datasets, i.e. those that present moderate variations in head pose, expression, illumination or partial occlusions, but may not be robust to "in-the-wild" data. In this paper, we address this problem by using an ensemble of deep regressors instead of a single large regressor. Furthermore, instead of averaging the outputs of each regressor, we propose an adaptive weighting scheme that uses a tree-structured gate. Experiments on several challenging face datasets demonstrate that our approach outperforms the state-of-the-art methods.