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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.


Face Detection Face Recognition Kernel Principal Component Analysis Kernel Support Vector

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How to Cite
Shaban Al-Ani, M. ., & Al-Waisy, A. S. . (2020). A Multi-View Face Detection Based on Kernel Principal Component Analysis and Kernel Support Vector Techniques. Convergence Chronicles, 1(4), 94–100.