## 3 Results  
### Setup  
Experiments were conducted on a desktop PC equipped with an Intel I7-9700K CPU (3.6 GHz), 8-GB RAM and an NVIDIA GeForce RTX1660 Ti GPU. The spatiotemporal correlation matching algorithm was implemented using C++ using Visual Studio 2015. Several function libraries (i.e., OpenMP, OpenCL and OpenGL) were used to develop our algorithm. The setup with a rotary speckle projector for capturing infrared (IR) speckle images is shown in Figure 7. The tested object was placed 500-mm directly in front of the projector. The device has two 200-Hz infrared light cameras with a baseline length of 120 mm. To avoid interference with human eyes and reduce the absorption of infrared light by the skin, the device was equipped with a 730-nm near-IR LED as speckle projection illumination, which is non-inductive to humans. IR cameras capture images simultaneously, whereas the rotary speckle projector projects speckle-encoded patterns onto a facial mask sequentially. The projected pattern number is consistent with the number of infrared stereo image pairs collected. The projector comprises an infrared light source, micromotor, pattern mask and lens group. A speckle pattern mask was generated by pseudo-random encoding. When the binocular camera acquires images synchronously, it is necessary to ensure that the speckle mask in the rotary projector remains stationary. The motor then drives the mask gear to rotate a certain angle (e.g., 9\({}^{\circ}\)) to ensure that there is no correlation between the continuously collected speckle encoding images, because the speckle patterns of the two pairs of continuous images are quite different. To reduce the motion blur caused by the rotating mask, the next set of image pairs is collected after the mask is completely still. The specific relationship between the rotation angle and the accuracy of 3D reconstruction is being studied by other members of our group, and this content is beyond the scope of this article. When the experimental device is operating, the acquisition time of infrared images depends heavily on the number of speckle patterns required by the spatiotemporal correlation algorithm. The exposure time of the infrared cameras was 2 ms, and the speckle pattern rotatory switching time was 11 ms for each image pair. Therefore, N \(\times\) 13 ms is required to acquire N pairs of stereo images. A 3D face acquisition device with a rotating speckle projector is shown in Figure 7.  
To overcome the problem of an excessively long acquisition time caused by the switching of the rotary speckle pattern, a compact 3D face acquisition device using a  
Figure 6: Flow diagram of images acquisition and 3-dimensional face generation. Figure 7: **(a)** Three-dimensional face acquisition equipment with rotary speckle projector. **(b)** Internal structure diagram of rotating speckle projector. fixed three-speckle projector was designed. A 3D face acquisition device with three fixed speckle projections is shown in Figure 8. The three fixed speckle projectors use different speckle masks and project the speckle patterns in sequence, and the two infrared cameras synchronously collect speckle-coded images in a time-sharing manner. The switching time of the speckle projection was reduced significantly. In the system, the interval for taking a single pair of speckle images was 5 ms, and three pairs of stereo-image acquisitions cost 15 ms total. The characteristics of short-time exposure and fast acquisition in the system showed good robustness and anti-interference characteristics for the 3D reconstruction of slight movements of the human face. The reason for adopting the structural design of three fixed speckle projections was deduced from the subsequent experimental analysis. Meanwhile, a high-precision industrial 3D scanner (ATOS Core 300 with measurement accuracy \(\pm\)0.02 mm) was used to collect 3D face mask data as ground truth, certified by VDI/VDE 2634 Part2 [40].