Three-dimensional Face Recognition Method Based on Multiresolution Model and Fuzzy Random Matrix

Three-dimensional face recognition is one of the hotspots of biometric recognition and has a wide range of applications in the fields of information security, video surveillance, video tracking, and so on. The core part of 3D face recognition is to establish the corresponding 3D face model, and the key of building the model is how to obtain the shape model and accurate texture mapping. The problem has not been well solved in the field of face reconstruction. Based on the background, this paper makes an in-depth study and proposes a three-dimensional face recognition method based on multiresolution model and fuzzy random matrix. The face model reconstruction method of image and model can accurately obtain the contour change of face and the representation of specific features through multiresolution model, reduce the inaccurate description of error and noise in samples through fuzzy random matrix, and enhance the effectiveness of image information of classification and recognition. The experimental outcomes exhibit that the 3D face focus approach based totally on multiresolution mannequin and fuzzy random matrix efficiently improves the evaluation effectivity of the model, improves the great of mannequin matching, improves the function extraction in the cognizance process, and improves the attention rate.