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Künstliche Intelligenz 33(4) - Dezember 2019

https://dl.gi.de/handle/20.500.12116/36218
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Auflistung Künstliche Intelligenz 33(4) - Dezember 2019 nach Autor:in "Bagchi, Parama"

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  • Zeitschriftenartikel
    Reg3DFacePtCd: Registration of 3D Point Clouds Using a Common Set of Landmarks for Alignment of Human Face Images
    (KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019) Bagchi, Parama; Bhattacharjee, Debotosh; Nasipuri, Mita
    The present work proposes a new method Reg3DFacePtCd for registration of point clouds. The key contribution of the present method is that an unknown face in 3D point cloud form is given to the system and is registered to the already existing known 3D face point clouds using a fast 3D face registration method. The novelty of the present technique is that at first the alignment and registration parameters are found out by initially registering eight key points of the unknown source model to that of the known model. Next, the rest of the point clouds of the unknown model are registered to that of the known model using the same parameters found as above. The main method used for alignment is iterative closest point (ICP) using point-based technique followed by registration in the least squares sense. Mainly there are two significant contributions. Firstly, we have developed a new mathematical model facial landmark point based model across poses to obtain the target or the known model to which all the unknown models will be registered. Secondly, a novel way to accelerate point cloud matching by reducing the number of points has been proposed. Using a small number of points necessarily would speed up the registration process but may inculcate errors. So, to determine the registration quality of the fundamental eight key points on which the entire registration process is based, a new robust metric namely ICV (ICP certainty vector) consisting of several key components have been used. Finally, we have addressed several important face registration issues like pre-processing, convergence and quality of registration of the entire facial point cloud based on the eight key points. Extensive experimentation on Frav3D, GavabDB, and the Bosphorus databases on a high-performance computing environment show the novelty and robustness of the method.