Invariant face detection with support vector machines

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.