Abstract
Traditional Iterative Closest Point (ICP) can not properly process the noise, outliers and missing data in face imaging, which would result in low accuracy of face image, face image registration error and much more noise in face image, to solve the above problems, an enhanced sparse ICP to register the 3D point clouds in face imaging is proposed. Sparse Iterative Closest Point (SICP) addressed these problems by formulating the registration optimization, which used sparsity inducing norms, moreover, a fast segmentation algorithm for head area segmentation in depth image was proposed. Based on the proposed fast segmentation algorithm and sparse ICP, a new real time 3D face modeling system was set up, which could generate real time 3D face models with high quality by using a depth camera (such as Kinect) even the background of face imaging was complicated.
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