Lucas-Kanade based entropy congealing for joint face alignment
Image and Vision Computing
Joint face alignment: rescue bad alignments with good ones by regularized re-fitting
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Joint face alignment with non-parametric shape models
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Digital paparazzi: spotting celebrities in professional photo libraries
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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As having multiple images of an object is practically convenient nowadays, to jointly align them is important for subsequent studies and a wide range of applications. In this paper, we propose a model-based approach to jointly align a batch of images of a face undergoing a variety of geometric and appearance variations. The principal idea is to model the non-rigid deformation of a face by means of a learned deformable model. Different from existing model-based methods such as Active Appearance Models, the proposed one does not rely on an accurate appearance model built from a training set. We propose a robust fitting method that simultaneously identifies the appearance space of the input face and brings the images into alignment. The experiments conducted on images in the wild in comparison with competing methods demonstrate the effectiveness of our method in joint alignment of complex objects like human faces.