Research On 3d Face Recognition Method In Nonspecific Environment

With the development of computer vision technology and artificial intelligence technology,face recognition has become a popular research topic in the field of computer vision.In nonspecific environments(i.e.,unconstrained environments),the face is prone to expression changes,pose changes and occlusions that affect the accuracy of face recognition.Compared with the traditional 2D face image data,3D face point cloud data contains richer information about the geometric form and spatial structure of the face,which can provide more effective data support for face recognition in non-specific environments.In this paper,a recognition method for common pose change,expression change,and occlusion issues in 3D face recognition in nonspecific environments is studied.The main contents of this paper are as follows:(1)The pose robust 3D face feature extraction method is studied.Firstly,the high-order curvature information of the 3D face surface is calculated,and the ridge valley points are extracted by combining the curvature extremum coefficients,and then the Gaussian curvature in the neighborhood of each ridge point is calculated,and the nasal tip points are detected according to the biological characteristics on the geometric structure of the nasal tip region and the Gaussian curvature distribution characteristics at the nasal tip.Experiments show that the method can also correctly detect the nasal tip points during posture changes,and the whole operation does not require a specific mathematical model.(2)The 3D face feature extraction method of expression is studied,and the profile features,middle profile features and ridge-valley line features of the 3D face are extracted respectively.The profile features can express the overall geometry of the face surface.The center contour line is the most representative contour line in the center of the 3D face,and the ridge and valley line are composed of ridge and valley points.The extraction of ridge and valley points depends on the high-order curvature features of the face surface with strong rotational invariance and high expression robustness.(3)An expression robust 3D face recognition method is proposed.By analyzing the face surface,the 3D face is divided into four regions: forehead,eyes,nose and mouth,and the ridge points in each region are characterized by the 3D shape context,and the matching cost is calculated in each region,and then the final recognition result is obtained by weighted fusion of the matching cost in each region.In this method,the description of ridge point features is comprehensive,the 3D shape context is used to make full use of the 3D information,and the fusion of sub-regions at the decision level takes into account the integrity of the face while reducing the influence of expression changes.Experiments show that this method has high recognition effect and robustness for different expression faces.(4)An occlusion conditions 3D face recognition method is proposed.For the common occlusion phenomenon of forehead,eye and mouth of human face,we first extract the ridge points of 3D face model to obtain the ridge point model of human face,divide the ridge point model of human face into several regions such as forehead,eye and mouth,and then determine whether there is occlusion in the region by extracting the spatial distribution features of ridge points in each region.For faces with occlusion,after eliminating the ridge points in the occlusion region,we further obtain the weighted 3D shape contextual features of the ridge points in different regions of the face for similarity calculation and obtain the recognition results.The experiments show that the method can achieve high recognition results for faces with the above three types of occlusions.