Near-infrared (NIR) fringe projection is gradually replacing visible fringe projection in face-scanning because NIR light is less harmful to human eyes and has a higher recognition rate in a special environment. However, since NIR is susceptible to interference from various heat sources and light sources, the NIR fringe image captured by the camera is of poor quality and has low contrast. And the captured low-quality fringe image will directly affect the quality of phase acquisition. Traditional phase acquisition methods, such as Fourier transform profilometry and phase-shifting profilometry, are difficult to achieve both high-speed and high-precision phase measurements at the same time. Therefore, this paper proposes a deep learning based phase acquisition method for NIR fringe projection. By using a deep learning model trained by the deep neural network with powerful learning and computing capabilities, phase extraction can be achieved from fewer NIR fringe images. Moreover, our method can retrieve the phase information with high speed and high quality without additional optimization of the original fringe map.
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