A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
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
Generic vs. person specific active appearance models
Image and Vision Computing
Active appearance models with occlusion
Image and Vision Computing
Non-rigid face tracking with enforced convexity and local appearance consistency constraint
Image and Vision Computing
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
ACM SIGGRAPH 2011 papers
Joint face alignment with a generic deformable face model
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Face image retrieval by shape manipulation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Localizing parts of faces using a consensus of exemplars
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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We present a joint face alignment technique that takes a set of images as input and produces a set of shape- and appearance-consistent face alignments as output. Our method is an extension of the recent localization method of Belhumeur et al. [1], which combines the output of local detectors with a non-parametric set of face shape models. We are inspired by the recent joint alignment method of Zhao et al. [20], which employs a modified Active Appearance Model (AAM) approach to align a batch of images. We introduce an approach for simultaneously optimizing both a local appearance constraint, which couples the local estimates between multiple images, and a global shape constraint, which couples landmarks and images across the image set. In video sequences, our method greatly improves the temporal stability of landmark estimates without compromising accuracy relative to ground truth.