Active shape models—their training and application
Computer Vision and Image Understanding
Automated Facial Expression Recognition Based on FACS Action Units
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Hierarchical Wavelet Networks for Facial Feature Localization
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Real-time View-based Face Alignment using Active Wavelet Networks
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Boosting Chain Learning for Object Detection
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Bayesian tangent shape model: Estimating shape and pose parameters via bayesian inference
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
An Efficient 3D Geometrical Consistency Criterion for Detection of a Set of Facial Feature Points
IEICE - Transactions on Information and Systems
A novel approach to classification of facial expressions from 3D-mesh datasets using modified PCA
Pattern Recognition Letters
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This paper presents a method to automatically locate facial feature points under large variations in pose, illumination and facial expressions. First we propose a method to calculate probabilistic-like output for each pixel of image. This probabilistic-like output describes the possibility of the pixel to be the center of specified object. A Gaussian Mixture Model is used to approximate the distribution of probabilistic-like output. The centers of these Gaussians are assigned with a probabilistic-like measure and they are considered as candidate feature points. There might be one or more candidate feature points in each facial region. A 3D model of facial feature points is built to enforce constraints on the localization results of feature points. Compared with Active Shape Model (ASM) and its variant methods, our method could accommodate larger variations in pose, lighting and face expressions. Moreover, it is less sensitive to initialization errors, accurate, and fast. It takes a computer with P4 CPU about 10ms to locate the five feature points (two eye centers, two mouth corners and nose tip). The feature localization accuracy is comparable with the accuracy of manually labeled features and it is robust to noise (glasses, beards). Experiments on FERET gallery and PIE are reported in this paper as well.