Abstract: The three-dimensional face measurement and segmentation have extensive application requirements and are the important research direction at present. However, the rapid development is constrained as the three-dimensional face data is huge and unordered. Firstly, the three-dimensional face measurement system based on the structured light method was developed, and the three-dimensional face data with high precision saved in the form of point cloud was obtained. Secondly, after the conformal transformation was adopted to preprocess the three-dimensional face data and the two-dimensional convolutional neural network segmentation combined with the three-dimensional inverse mapping was adopted, the three-dimensional face segmentation was realized, the disorder and rotation of three-dimensional data were solved, and the time consumption of three-dimensional data segmentation was reduced. The experimental results show that the proposed precision of three-dimensional face measurement system can reach to 0.5 mm, and the average intersection-over-union (IoU) of three-dimensional segmentation can reach to 0.78. The overall efficiency of two-dimensional segmentation combined with the three-dimensional inverse mapping is obviously higher than that of the three-dimensional segmentation.