Face pose discrimination with support vector machines
Guangyou Xu · Journal of Tsinghua University(Science and Technology) · 2003
An approach for face pose discrimination using support vector machines (SVM) is proposed for the multiview face detection. Six different types of poses were defined with 1 800 images from a multiview face database as the training set and another 300 images as the test set. Classifiers based on support vector classification (SVC) had a 1.67% error rate while those based on support vector regression had a 3.33% error rate. The SVC performance was superior to an artificial neural network classifier (3.33% error), which performed best among traditional pattern classifiers. Thus the experiments demonstrated that SVM is a feasible approach for face pose discrimination.