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In this paper, we propose Deep Alignment Network (DAN), a robust facealignment method based on a deep neural network architecture. DAN consists ofmultiple stages, where each stage improves the locations of the faciallandmarks estimated by the previous stage. Our method uses entire face imagesat all stages, contrary to the recently proposed face alignment methods thatrely on local patches. This is possible thanks to the use of landmark heatmapswhich provide visual information about landmark locations estimated at theprevious stages of the algorithm. The use of entire face images rather thanpatches allows DAN to handle face images with large variation in head pose anddifficult initializations. An extensive evaluation on two publicly availabledatasets shows that DAN reduces the state-of-the-art failure rate by up to 70%.Our method has also been submitted for evaluation as part of the Menpochallenge.

Deep Alignment Network: A convolutional neural network for robust face alignment