Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Recognition of Planar Object Classes
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Rule-based face detection in frontal views
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Probabilistic visual learning for object detection
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Feature-Based Affine-Invariant Localization of Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis and support vector machine for face feature extraction
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Hypotheses-driven affine invariant localization of faces in verification systems
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Active contour and morphological filters for geometrical normalization of human face
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
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We propose a novel framework for detecting human faces based on correspondences between triplets of detected local features and their counterparts in an affine invariant face appearance model.Th e method is robust to partial occlusion, feature detector failure and copes well with cluttered background. Both the appearance and configuration probabilities are learned from examples. The method was tested on the XM2VTS database and a limited number of images with cluttered background with promising results - 2% false negative rate - was obtained.