2012 | OriginalPaper | Buchkapitel
Joint Face Alignment: Rescue Bad Alignments with Good Ones by Regularized Re-fitting
verfasst von : Xiaowei Zhao, Xiujuan Chai, Shiguang Shan
Erschienen in: Computer Vision – ECCV 2012
Verlag: Springer Berlin Heidelberg
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Nowadays, more and more applications need to jointly align a set of facial images from one specific person, which forms the so-called
joint face alignment
problem. To address this problem, in this paper, starting from an initial face alignment results, we propose to enhance the alignments by a fundamentally novel idea:
rescuing the bad alignments with their well-aligned neighbors
. In our method, a discriminative alignment evaluator is well designed to assess the initial face alignments and separate the well-aligned images from the badly-aligned ones. To correct the bad ones, a robust regularized re-fitting algorithm is proposed by exploiting the appearance consistency between the badly-aligned image and its
k
well-aligned nearest neighbors. Experiments conducted on faces in the wild demonstrate that our method greatly improves the initial face alignment results of an off-the-shelf facial landmark locator. In addition, the effectiveness of our method is validated through comparing with other state-of-the-art methods in joint face alignment under complex conditions.