Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A statistical approach to 3d object detection applied to faces and cars
A statistical approach to 3d object detection applied to faces and cars
Robust Real-Time Face Detection
International Journal of Computer Vision
Boosted multi image features for improved face detection
AIPR '08 Proceedings of the 2008 37th IEEE Applied Imagery Pattern Recognition Workshop
Ensemble learning with biased classifiers: the Triskel algorithm
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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In this paper, we briefly review AdaBoost and expand on the Discrete version by building weak classifiers from a pair of biased classifiers which enable the weak classifier to abstain from classifying some samples. We show that this approach turns into a 3-bin Real AdaBoost approach where the bin sizes and positions are set by the bias parameters selected by the user and dynamically change with every iteration which make it different from the traditional Real AdaBoost. We apply this method to face detection more specifically the Viola-Jones approach to detecting faces with Haar-like features and empirically show that our method can help improving the generalization ability by reducing the testing error of the final classifier. We benchmark the results on the MIT+CMU database.