Efficient Face Detection by a Cascaded Support Vector Machine using Haar-like Features
Matthias Raetsch, Sami Romdani and Thomas Vetter
In: DAGM'04: 26th Pattern Recognition Symposium, 30 Aug - 1 Sep, Tübingen Germany.
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.