Active shape models—their training and application
Computer Vision and Image Understanding
Mixtures of probabilistic principal component analyzers
Neural Computation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Data Driven Image Models through Continuous Joint Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face swapping: automatically replacing faces in photographs
ACM SIGGRAPH 2008 papers
A Generative Shape Regularization Model for Robust Face Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Face Alignment Via Component-Based Discriminative Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Joint face alignment with a generic deformable face model
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A rank-order distance based clustering algorithm for face tagging
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Face reconstruction in the wild
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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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.