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
Robust Real-Time Face Detection
International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
High-Performance Rotation Invariant Multiview Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast rotation invariant multi-view face detection based on real adaboost
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Reducing SVM classification time using multiple mirror classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Branching competitive learning Network: A novel self-creating model
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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This paper proposes a self-created multi-layer cascaded architecture for multi-view face detection. Instead of using predefined a priori about face views, the system automatically divides the face sample space using the kernel-based branching competitive learning (KBCL) network at different discriminative resolutions. To improve the detection efficiency, a coarse-to-fine search mechanism is involved in the procedure, where the boosted mirror pair of points (MPP) classifiers is employed to classify image blocks at different discriminatory levels. The boosted MPP classifiers can approximate the performance of the standard support vector machines in a hierarchical way, which allows background blocks to be excluded quickly by simple classifiers and the ‘face like' parts remained to be judged by more complicate classifiers. Experimental results show that our system provides a high detection rate with a particularly low level of false positives.