Abstract
In this paper, we present a novel method for reducing the computational complexity of a Support Vector Machine (SVM) classifier without significant loss of accuracy. We apply this algorithm to the problem of face detection in images. To achieve high run-time efficiency, the complexity of the classifier is made dependent on the input image patch by use of a Cascaded Reduced Set Vector expansion of the SVM. The novelty of the algorithm is that the Reduced Set Vectors have a Haar-like structure enabling a very fast SVM kernel evaluation by use of the Integral Image. It is shown in the experiments that this novel algorithm provides, for a comparable accuracy, a 200 fold speed-up over the SVM and an 6 fold speed-up over the Cascaded Reduced Set Vector Machine.
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© 2004 Springer-Verlag Berlin Heidelberg
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Rätsch, M., Romdhani, S., Vetter, T. (2004). Efficient Face Detection by a Cascaded Support Vector Machine Using Haar-Like Features. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_8
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DOI: https://doi.org/10.1007/978-3-540-28649-3_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22945-2
Online ISBN: 978-3-540-28649-3
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