A three-dimensional face reconstruction method based on grayscale and depth information, mainly comprising: performing identification with respect to grayscale information of a face; performing identification with respect to depth information of the face; performing multi-modal face identification; performing a matching operation by means of a 3D model; and performing 3D reconstruction of the face. The method comprises the processes of: locating a feature area with face data and performing registration and feature extraction by using a feature point; using the Adaboost algorithm to select a most effective feature for classification; using a nearest neighbor classifier to calculate a matching score so as to realize the multi-modal face identification; and completing face reconstruction by matching a local 3D model. The method effectively improves the performance and the efficiency of a face identification system by using a fused strategy. By utilizing 3D-based cascade regression, and selecting a dense 3D point set, the present invention enables a face to be completely marked to prevent a position change of a marker, thus solving the issues of movement-caused positioning point inconsistency and self-closure. The present invention reduces the computational costs, is highly versatile, and has superior real-time performance.
THREE-DIMENSIONAL FACE RECONSTRUCTION METHOD BASED ON GRAYSCALE AND DEPTH INFORMATION