Adaptive histogram equalization and its variations
Computer Vision, Graphics, and Image Processing
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Pictorial Structures for Object Recognition
International Journal of Computer Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
3D Alignment of Face in a Single Image
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Real-time facial feature localization by combining space displacement neural networks
Pattern Recognition Letters
Learning Local Objective Functions for Robust Face Model Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic feature localisation with constrained local models
Pattern Recognition
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable Model Fitting by Regularized Landmark Mean-Shift
International Journal of Computer Vision
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Real-Time Facial Feature Tracking on a Mobile Device
International Journal of Computer Vision
Neural network cascade for facial feature localization
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in 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
Face detection, pose estimation, and landmark localization in the wild
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Face alignment by Explicit Shape Regression
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Facial landmark detection in uncontrolled conditions
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
Robust and accurate shape model fitting using random forest regression voting
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Local Evidence Aggregation for Regression-Based Facial Point Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generic active appearance models revisited
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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Automatic facial landmarking is a crucial prerequisite of many applications dedicated to face analysis. In this paper we describe a two-step method. In a first step, each landmark position in the image is predicted independently. To achieve fast and accurate localizations, we implement detectors based on a two-stage classifier and we use multiple kernel learning algorithms to combine multi-scale features. In a second step, to increase the robustness of the system, we introduce spatial constraints between landmarks. To this end, parameters of a deformable shape model are optimized using the first step outputs through a Gauss-Newton algorithm. Extensive experiments have been carried out on different databases (PIE, LFPW, Cohn-Kanade, Face Pix and BioID), assessing the accuracy and the robustness of the proposed approach. They show that the proposed algorithm is not significantly affected by small rotations, facial expressions or natural occlusions and can be favorably compared with the current state of the art landmarking systems.