Active shape models—their training and application
Computer Vision and Image Understanding
Mixtures of probabilistic principal component analyzers
Neural Computation
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interpreting Face Images Using Active Appearance Models
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Active Appearance Models Revisited
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Locating Facial Features with an Extended Active Shape Model
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
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
Deformable Model Fitting by Regularized Landmark Mean-Shift
International Journal of Computer Vision
Image and Vision Computing
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Although the conventional Active Appearance Model (AAM) has achieved some success for face alignment, it still suffers from the generalization problem when be applied to unseen subjects and images. In this paper, a novel Locality-constraint AAM (LC-AAM) algorithm is proposed to tackle the generalization problem of AAM. Theoretically, the proposed LC-AAM is a fast approximation for a sparsity-regularized AAM problem, where sparse representation is exploited for non-linear face modeling. Specifically, for an input image, its K-nearest neighbors are selected as the shape and appearance bases, which are adaptively fitted to the input image by solving a constrained AAM-like fitting problem. Essentially, the effectiveness of our LC-AAM algorithm comes from learning a strong localized shape and appearance prior for the input facial image through exploiting its K-similar patterns. To validate the effectiveness of our algorithm, comprehensive experiments are conducted on two publicly available face databases. Experimental results demonstrate that our method greatly outperforms the original AAM method and its variants. In addition, our method is better than the state-of-the-art face alignment methods and generalizes well to unseen subjects and images.