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Multi-pose face recognition based on virtual samples of local weighted mean

Zhang Yousai,Yang Shu(School of Electronics and Information,Jiangsu University of Science and Technology,Zhenjiang Jiangsu 212003,China)  
To solve the problem of generating multi-pose faces in multi-pose face recognition,we propose a multi-pose face generation algorithm based on local weighted mean method.We find the mapping function set of local feature points between front face and multi-pose face by applying polynomial fitting,and then design the transform function of each pixel for generating the multi-pose face by using weighted mean of mapping functions of neighboring feature points.At last,we chose principal component analysis to extract face feature vectors and utilize support vector machine to recognize the multi-pose faces.Our approach overcomes the difficulty of obtaining multi-pose face images in multi-pose face recognition and solves the problem of the rapid fall of recognition rate due to the face pose change.Experimental results show that the multi-pose faces produced by local weighted mean algorithm preserve the local features of faces and have a high similarity with the original faces in library ORL,thus effectively improving multi-pose face recognition rate.
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