Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Learning of Multi-view Face Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Coordinating Principal Component Analyzers
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Automated derivation of behavior vocabularies for autonomous humanoid motion
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Fast frontal-view face detection using a multi-path decision tree
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A Bayesian discriminating features method for face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust multipose face detection in images
IEEE Transactions on Circuits and Systems for Video Technology
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The development of web and digital camera nowadays has made it easier to collect more than hundreds of thousands of examples. How to train a face detector based on the collected enormous face database? This paper presents a manifold-based method to subsample. That is, we learn the manifold from the collected face database and then subsample training set by the estimated geodesic distance which is calculated during the manifold learning. Using the subsampled training set based on the manifold, we train an AdaBoost-based face detector. The trained detector is tested on the MIT+CMU frontal face test set. The experimental results show that the proposed method is effective and efficient to train a classifier confronted with the huge database.