International Journal of Computer Vision
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast algorithm for tracking human faces based on chromatic histograms
Pattern Recognition Letters
Segmenting Hands of Arbitrary Color
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Robust Face Tracking Using Color
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Detecting and Tracking Human Faces in Videos
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
A detector tree of boosted classifiers for real-time object detection and tracking
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
CamShift guided particle filter for visual tracking
Pattern Recognition Letters
Bilayer Segmentation of Webcam Videos Using Tree-Based Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-cue-based CamShift guided particle filter tracking
Expert Systems with Applications: An International Journal
Fast rotation invariant multi-view face detection based on real adaboost
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
| Hi-index | 12.05 |
Face localization is the first stage in many vision based applications and in human-computer interaction. The problem is to define the face location of a person in a color image. The four boosted classifiers embbeded in OpenCV, based on Haar-like features, are compared in terms of speed and efficiency. Skin color distribution is estimated using a non parametric approach. To avoid drifting in color estimate, this model is not updated during the sequence but renewed whenever the face is detected again, that gives the ability to our system to cope with different lighting conditions in a more robust way. Skin color model is then used to localize the face represented by an ellipse: connected component segmentation and a statistical approach, namely the coupled Camshift of Bradsky, are compared in terms of efficiency and speed. The pursuit algorithms are tested on various video sequences, corresponding to various scenarios in terms of illumination, face pose, face size and background complexity (distractor effects).