Abstract
This paper presents a new approach to automatic 3D face recognition using a model-based approach. This work uses real 3D dense point cloud data acquired with a scanner using a stereo photogrammetry technique. Since the point clouds are in varied orientations, by applying a non-iterative registration method, we automatically transform each point cloud to a canonical position. Unlike the iterative ICP algorithm, our non-iterative registration process is scale invariant. An efficient B-spline surface-fitting technique is developed to represent 3D faces in a way that allows efficient surface comparison. This is based on a novel knot vector standardisation algorithm which allow a single BSpline surface to be fitted onto a complex object represented as a unstructured points cloud. Consequently, dense correspondences across objects are established. Several experiments have been conducted and 91% recognition rate can be achieved.
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Song, Y., Bai, L. (2006). 3D Face Recognition Based on Non-iterative Registration and Single B-Spline Patch Modelling Techniques. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_72
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DOI: https://doi.org/10.1007/11864349_72
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