High-Speed Phase-Shifting 3D Profilometry on Human Face Assisted by Statistical Model

Phase-shifting 3D profilometry is of high precision, but its performance could be far below expectations due to its long exposure time in the measurement of dynamic objects, especially human faces. In this article, a new idea is proposed to improve the measuring speed by providing reference derived from the statistical characteristics of human faces for the reconstruction of faces measured. During the measurement, no Gray code or low-frequency fringe is required, which reduces the number of patterns projected and sharply shortens the measuring time. Assisted by face detection and point cloud clustering, several candidate faces of different orders can be generated from the wrapped phase. Afterwards, the most probable candidate is selected according to the probability model of human face to obtain the correct result. Furthermore, the applicable conditions of our method have been analyzed, and the validity has been proved by experiments on actual human faces as well.

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