This paper present an analysis of the performance of support vector machines (SVMs) for automatic detection of human faces in static color images of complex scenes. Skin color-based image segmentations initially performed for several different chrominance spaces by use of the single Gaussian chrominance model and a Gaussian mixture density model. Feature extraction in the segmented images is then implemented by use of invariant orthogonal Fourier-Mellin moments. For all chrominance spaces, the application of SVMs to the invariant moments obtained from a set of 100 test images yields a higher face detection performance than when applying a 3-layer perceptron neural network (NN), depending on a suitable selection of the kernel function used to train the SVM and of the value of its associated parameter(s). The training of SVMs is easier and faster than that of a NN, always finds a global minimum, and SVMs have a better generalization ability.
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