Floating search methods in feature selection
Pattern Recognition Letters
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting as entropy projection
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Statistical Learning of Multi-view Face Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Learning Human Face Detection in Cluttered Scenes
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Boosting with averaged weight vectors
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Detection of Dogs in Video Using Statistical Classifiers
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
Cascade boosting-based object detection from high-level description to hardware implementation
EURASIP Journal on Embedded Systems - Special issue on design and architectures for signal and image processing
Pedestrian recognition from a moving catadioptric camera
Proceedings of the 29th DAGM conference on Pattern recognition
Online boosting for vehicle detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Framework for research on detection classifiers
Proceedings of the 24th Spring Conference on Computer Graphics
Real-time object detection on CUDA
Journal of Real-Time Image Processing
Fast head tilt detection for human-computer interaction
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
A review of recent advances in 3D ear- and expression-invariant face biometrics
ACM Computing Surveys (CSUR)
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An extension of the AdaBoost learning algorithm is proposed and brought to bear on the face detection problem. In each weak classifier selection cycle, the novel totally corrective algorithm reduces aggressively the upper bound on the training error by correcting coefficients of all weak classifiers. The correction steps are proven to lower the upper bound on the error without increasing computational complexity of the resulting detector. We show experimentally that for the face detection problem, where large training sets are available, the technique does not overfit. A cascaded face detector of the Viola-Jones type is built using AdaBoost with the Totally Corrective Update. The same detection and false positive rates are achieved with a detector that is 20% faster and consists of only a quarter of the weak classifiers needed for a classifier trained by standard AdaBoost. The latter property facilitates hardware implementation, the former opens scope for the increase in the search space, e.g. the range of scales at which faces are sought.