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Detecting faces across multiple views is more challenging than in a frontal view. To address this problem, an efficient approach is presented in this paper using a kernel machine-based approach for learning such nonlinear mappings to provide effective view-based representation for multi-view face detection. In this paper Kernel Principal Component Analysis (KPCA) is used to project data into the view-subspaces then computed as view-based features. Multi-view face detection is performed by classifying each input image into the face or non-face class, by using a two-class Kernel Support Vector Classifier (KSVC). Experimental results demonstrate successful face detection over a wide range of facial variations in color, illumination conditions, position, scale, orientation, 3D pose, and expression in images from several photo collections.
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