Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Antifaces: A Novel, Fast Method for Image Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Summed-area tables for texture mapping
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Face Detection in Color Images using Wavelet Packet Analysis
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Greedy-based design of sparse two-stage SVMs for fast classification
Proceedings of the 29th DAGM conference on Pattern recognition
Efficient object tracking by condentional and cascaded image sensing
Computer Standards & Interfaces
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In this paper, we present a novel algorithm for reducing the runtime computational complexity of a Support Vector Machine classifier. This is achieved by approximating the Support Vector Machine decision function by an over-complete Haar wavelet transformation. This provides a set of classifiers of increasing complexity that can be used in a cascaded fashion yielding excellent runtime performance. This over-complete transformation finds the optimal approximation of the Support Vectors by a set of rectangles with constant gray-level values (enabling an Integral Image based evaluation). A major feature of our training algorithm is that it is fast, simple and does not require complicated tuning by an expert in contrast to the Viola & Jones classifier. The paradigm of our method is that, instead of trying to estimate a classifier that is jointly accurate and fast (such as the Viola & Jones detector), we first build a classifier that is proven to have optimal generalization capabilities; the focus then becomes runtime efficiency while maintaining the classifier’s optimal accuracy. We apply our algorithm to the problem of face detection in images but it can also be used for other image based classifications. We show that our algorithm provides, for a comparable accuracy, a 15 fold speed-up over the Reduced Support Vector Machine and a 530 fold speed-up over the Support Vector Machine, enabling face detection at 25 fps on a standard PC.