Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection From Color Images Using a Fuzzy Pattern Matching Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Robust Face Detection at Video Frame Rate Based on Edge Orientation Features
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Multi-Modal Face Tracking Using Bayesian Network
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Shape-Based Recognition of Wiry Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Binary Tree for Probability Learning in Eye Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Learning a restricted Bayesian network for object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
Simultaneous eye tracking and blink detection with interactive particle filters
EURASIP Journal on Advances in Signal Processing
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This paper presents an integrated approach for robustly loca ting facial landmark for drivers. In the first step a cascade of probability learners is used to detect the face edge primitives from fine to coarse, so that faces with variant head poses can be located. The edge density descriptors and skin-tone color features are combined together as the basic features to examine the probability of an edge being a face primitive. A cascade of the probability learner is used. In each scale, only edges with sufficient large probabilities are kept and passed on to the next scale. The final output of the cascade gives the edge primitives that belong to faces, which determine the face location. In the second step, a facial landmark detection procedure is applied on the segmented face pixels. Facial landmark candidates are first detected by learning the posteriors in multiple resolutions. Then geometric constraint and the local appearance, modeled by SIFT descriptor, are used to find the set of facial landmarks with largest matching score. Experiments over high-resolution images (FERET database) as well as the real-world drivers’ data are used to evaluate the performance. A fairly good results can be obtained, which validates the proposed approach.