Face images are subject to changes in view and illumination. Such changes cause data distribution to be highly nonlinear and complex in the image space. It is desirable to learn a nonlinear mapping from the image space to a low dimensional space such that the distribution becomes simpler tighter and therefore more predictable for better modeling effaces. In this paper we present a kernel machine based approach for learning such nonlinear mappings. The aim is to provide an effective view-based representation for multi-view face detection and pose estimation. Assuming that the view is partitioned into a number of distinct ranges, one nonlinear view-subspace is learned for each (range of) view from a set of example face images of that view (range), by using kernel principal component analysis (KPCA). Projections of the data onto the view-subspaces are then computed as view-based nonlinear features. Multi-view face detection and pose estimation are performed by classifying a face into one of the facial views or into the nonface class, by using a multi-class kernel support vector classifier (KSVC). Experimental results show that fusion of evidences from multi-views can produce better results than using the result from a single view; and that our approach yields high detection and low false alarm rates in face detection and good accuracy in pose estimation, in comparison with the linear counterpart composed of linear principal component analysis (PCA) feature extraction and Fisher linear discriminant based classification (FLDC).
['Stan Z. Li', 'QingDong Fu', 'Lie Gu', 'Bernhard Schölkopf', 'Yimin Cheng', 'Hong-Jiang Zhang']
Kernel machine based learning for multi-view face detection and pose estimation