Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
A Comparative Study of Cost-Sensitive Boosting Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Robust Real-Time Face Detection
International Journal of Computer Vision
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Learning classifiers from imbalanced data based on biased minimax probability machine
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Pareto optimal linear classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Hierarchy of Support Vector Machines for Pattern Detection
The Journal of Machine Learning Research
On the Design of Cascades of Boosted Ensembles for Face Detection
International Journal of Computer Vision
Floatcascade learning for fast imbalanced web mining
Proceedings of the 17th international conference on World Wide Web
Catenary Support Vector Machines
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Improving object detection by removing noisy samples from training sets
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
LACBoost and FisherBoost: optimally building cascade classifiers
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
On asymmetric classifier training for detector cascades
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Towards optimal training of cascaded detectors
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Asymmetric constraint optimization based adaptive boosting for cascade face detector
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Creating robust high-throughput traffic sign detectors using centre-surround HOG statistics
Machine Vision and Applications
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The detection of faces in images is fundamentally a rare event detection problem. Cascade classifiers provide an efficient computational solution, by leveraging the asymmetry in the distribution of faces vs. non-faces. Training a cascade classifier in turn requires a solution for the following subproblems: Design a classifier for each node in the cascade with very high detection rate but only moderate false positive rate. While there are a few strategies in the literature for indirectly addressing this asymmetric node learning goal, none of them are based on a satisfactory theoretical framework. We present a mathematical characterization of the node-learning problem and describe an effective closed form approximation to the optimal solution, which we call the Linear Asymmetric Classifier (LAC). We first use AdaBoost or AsymBoost to select features, and use LAC to learn a linear discriminant function to achieve the node learning goal. Experimental results on face detection show that LAC can improve the detection performance in comparison to standard methods. We also show that Fisher Discriminant Analysis on the features selected by AdaBoost yields better performance than AdaBoost itself.