Robust regression and outlier detection
Robust regression and outlier detection
Active shape models—their training and application
Computer Vision and Image Understanding
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ranking Prior Likelihood Distributions for Bayesian Shape Localization Framework
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Joint Haar-like Features for Face Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Face Recognition by Projection-based 3D Normalization and Shading Subspace Orthogonalization
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Performance evaluation and optimization for content-based image retrieval
Pattern Recognition
Locating Facial Features with an Extended Active Shape Model
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Facial feature detection using distance vector fields
Pattern Recognition
Automatic 3D reconstruction for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Automatic detection of facial feature points via HOGs and geometric prior models
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Wide range face pose estimation by modelling the 3D arrangement of robustly detectable sub-parts
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Face recognition technology and its real-world application
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Image and Vision Computing
Automatic detailed localization of facial features
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Nearest neighbor weighted average customization for modeling faces
Machine Vision and Applications
Facial expression recognition based on anatomy
Computer Vision and Image Understanding
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We propose an efficient and generic facial feature localization method based on a weighted vector concentration approach. Our method does not require any specific priors on facial shape but implicitly learns its structural information from a training data. Unlike previous work, facial feature points are globally estimated by the concentration of directional vectors from sampling points on a face region, and those vectors are weighted by using local likelihood patterns which discriminate the appropriate position of the feature points. The directional vectors and local likelihood patterns are provided through nearest neighbor search between local patterns around the sampling points and a trained codebook of extended templates. The combination of the global vector concentration and the verification with the local likelihood patterns achieves robust facial feature point detection. We demonstrate that our method outperforms state-of-the-art method based on the Active Shape Models in our evaluation.