Existing speckle-encoding structured light 3D measurement methods can be divided into two types [30]: temporal- and spatial-domain. Temporal-domain encoding structured light encodes the entire image along the time axis by successively projecting multiple pre-set patterns. The measurement accuracy of the system depends heavily on the number of projection patterns. The spatial-domain method only collects a set of stereo image pairs and makes full use of the correlation information of adjacent pixels where the accuracy is susceptible to the steep surface of the measured object. The 3D measurement of the single-shot speckle method mentioned earlier mostly adopts spatial domain correlation methods, and its calculation accuracy is limited to a certain extent. To improve the measurement accuracy of speckle-structured light, speckle-coded image-based spatiotemporal correlation computation has become a research hotspot. [PERSON] et al. [30] proposed a pattern system based on temporal and spatial encoding, which can measure slow-moving objects. Meanwhile, temporal and spatial encoding schemes are designed according to different speed and accuracy requirements. [PERSON] et al. [31] comprehensively utilised the characteristics of two methods to find a pair of homologous points through the temporal correlation of the grey value sequence, where spatial correlation was used to verify the correctness of the homologous points. Under equivalent accuracy, the model can reduce the spatial correlation area and make it suitable for the surface reconstruction of objects with large fluctuations. Mainly, it reduces the number of images required for 3D reconstruction, which makes it possible to reconstruct dynamic scenes. [PERSON] et al. [32] proposed a weighted spatiotemporal correlation method to reconstruct static or moving objects, and the weight value was used to adjust the spatial and temporal parameters of the matching region. [PERSON] et al. [33] evaluated the relationship between the size of the correlation area and the number of projection patterns. Experiments showed that selecting an appropriate window and a reasonable number of speckle patterns produced more accurate results. However, they only analysed the dumbbell gauges, and their results were not universally generalisable for face modelling. The 3D measurement system proposed by [PERSON] et al. [34] consists of an expensive industrial camera and a motor-driven projection device with high-density speckles to ensure the integrity and accuracy of the 3D face. However, the device is too large and expensive for routine use, and the matching procedure is not optimised. Its computation speed is slow, and it cannot meet the application requirements of a portable 3D face recognition system. [PERSON] et al. [35] projected a visible-light speckle pattern on the human face and utilised a spatiotemporal stereo matching algorithm to achieve fast 3D face reconstruction. However, it only projected three frames of speckle patterns, and the selected parameters were not optimally evaluated, which limits reconstruction accuracy. To quickly obtain a high-precision real 3D face model, the authors conducted research on measurement methods and parallel acceleration processing strategies. The contribution of this study is to the following three aspects:  
1. A framework for implementing a high-precision 3D face imaging technique with a coarse-to-fine spatiotemporal correlation is designed. A spatiotemporal box filter is used to improve the spatiotemporal correlation stereo matching process and accelerate the calculation. 2. The relationship between the number of projected speckle patterns, matching window size, reconstruction accuracy and time cost in the spatiotemporal correlation 3D face imaging method is illuminated through experiments. The optimal combination of parameters to meet the requirements of a balanced strategy of reconstruction speed and accuracy is obtained. A reference for real-time high-precision 3D reconstruction is provided in the next step. 3.