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-the-art failure rate by up to 70 . Our method has also been submitted for evaluation as part of the Menpo challenge.
While all of the above mentioned methods perform face alignment based only on local patches, there are some methods @cite_44 @cite_46 that estimate initial landmark positions using the entire face image and use local patches for refinement. In contrast, DAN localizes the landmarks based on the entire face image at all of its stages.
@cite_44 @cite_46
2623892189 2962819150 169095154 2012885984
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-the-art failure rate by up to 70 . Our method has also been submitted for evaluation as part of the Menpo challenge. 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-the-art failure rate by up to 70 . Our method has also been submitted for evaluation as part of the Menpo challenge. Facial landmark detection of face alignment has long been impeded by the problems of occlusion and pose variation. Instead of treating the detection task as a single and independent problem, we investigate the possibility of improving detection robustness through multitask learning. Specifically, we wish to optimize facial landmark detection together with heterogeneous but subtly correlated tasks, e.g. head pose estimation and facial attribute inference. This is non-trivial since different tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, with task-wise early stopping to facilitate learning convergence. Extensive evaluations show that the proposed task-constrained learning (i) outperforms existing methods, especially in dealing with faces with severe occlusion and pose variation, and (ii) reduces model complexity drastically compared to the state-of-the-art method based on cascaded deep model [29]. In this technical report, we extend the method presented in our ECCV 2014 [39] paper to handle more landmark points (68 points instead of 5 major facial points) without either redesigning the deep model or involving significant increase in run time cost. This is made possible by transferring the learned 5-point model to the desired facial landmark configuration, through model fine-tuning with dense landmark annotations. Our new model achieves the state-of-the-art result on the 300-W benchmark dataset (mean error of 9.15 on the challenging IBUG subset). Face alignment is a crucial step in face recognition tasks. Especially, using landmark localization for geometric face normalization has shown to be very effective, clearly improving the recognition results. However, no adequate databases exist that provide a sufficient number of annotated facial landmarks. The databases are either limited to frontal views, provide only a small number of annotated images or have been acquired under controlled conditions. Hence, we introduce a novel database overcoming these limitations: Annotated Facial Landmarks in the Wild (AFLW). AFLW provides a large-scale collection of images gathered from Flickr, exhibiting a large variety in face appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions. In total 25,993 faces in 21,997 real-world images are annotated with up to 21 landmarks per image. Due to the comprehensive set of annotations AFLW is well suited to train and test algorithms for multi-view face detection, facial landmark localization and face pose estimation. Further, we offer a rich set of tools that ease the integration of other face databases and associated annotations into our joint framework.