This paper presents an analysis of the performance of Support Vector Machines (SVMs) for the automatic detection of human faces in static color images of complex scenes. SVMs are a new interesting type of binary classifier based on a novel statistical learning technique that has been developed in recent years by V. Vapnik et al. at AT&T Bell Labs [2] [4] [6] [22]. Skin color-based image segmentation is initially performed for several different chrominance spaces by use of the single Gaussian chrominance model and of a Gaussian mixture density model, as described in [17]. Feature extraction in the segmented images is then implemented by use of invariant Orthogonal Fourier-Mellin Moments (OFMMs) [16] [20]. 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 [5].