Objective To address the deformation issues in non-rigid registration algorithms for the three-dimensional reconstruction of facial and neck regions this paper designed a method for collecting point clouds of the facial and neck regions and proposed an adaptive local registration algorithm that restores global registration. Methods A depth camera was used to collect point clouds at three positions of the head the right mandibular angle the left mandibular angle and the nasal bridge. The collected point clouds were preprocessed and the RGB information of the point cloud was converted into HSV Hue Saturation Value . The position of the lips was located and the facial area was segmented to retain the nose and mouth regions. The random sample consensus RANSAC algorithm combined with the three-dimensional shape context 3DSC feature was applied for coarse registration followed by the iterative closest point ICP algorithm for fine registration. Finally the transformation matrix obtained from local registration was applied to the original point cloud to obtain a three-dimensional point cloud model of the facial and neck regions. Results Experimental results showed that the point clouds collected at the three positions could fully cover the entire facial and neck region. By comparing five improved iterative closest point registration algorithms it was found that the registration accuracy was the highest when using the 3DSC+RANSAC+ICP algorithm. By setting facial and neck landmarks and conducting registration experiments with different facial data the registration results were compared with the distances between the marked points on the real face with errors all less than 2. 5 mm verifying the registration accuracy and robustness of the algorithm. Conclusion The designed multi-view facial and neck registration algorithm can effectively register with registration errors less than 2. 5 mm compared with real faces addressing the deformation issues in non-rigid registration algorithms for three- dimensional reconstruction of facial and neck regions and demonstrating certain robustness when processing facial and neck data from different individuals.