Kernel machine based learning for multi-view face detection and pose estimation

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Abstract
Face images are subject to changes in view and illumi- nation. 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 di- mensional space such that the distribution becomes simpler, tighter and therefore more predictable for better modeling of faces. 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). Projec- tions 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). Ex- perimental 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 ac- curacy in pose estimation, in comparison with the linear counterpart composed of linear principal component anal- ysis (PCA) feature extraction and Fisher linear discriminant based classification (FLDC).

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