In this paper, we propose Deep Alignment Network (DAN), a robust face alignment method based on a deep neural network architecture. DAN consists of multiple stages, where each stage improves the locations of the facial landmarks estimated by the previous stage. Our method uses entire face images at all stages, contrary to the recently proposed face alignment methods that rely on local patches. This is possible thanks to the use of landmark heatmaps which provide visual information about landmark locations estimated at the previous stages of the algorithm. The use of entire face images rather than patches allows DAN to handle face images with large variation in head pose and difficult initializations. An extensive evaluation on two publicly available datasets shows that DAN reduces the state-of-theart failure rate by up to 70%. Our method has also been submitted for evaluation as part of the Menpo challenge.
Our approach builds up on the Deep Alignment Network architecture REF , initially proposed for robust face alignment.
Deep Alignment Network: A Convolutional Neural Network for Robust Face Alignment
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Computer Science