Research On Face Recognition Based On Contour Lines

Face recognition is one of the most active and challenging technologies. As a natural and friendly way, automatic face recognition has become an important part of the researches of image processing and pattern recognition.Because of the influence of illumination, pose variation and expression, the improvement of recognition accuracy of 2D face recognition is greatly impeded and it is difficult to build a robust face recognition system. Due to its richer information contained for facial surface, the 3D face data has more promising potential to conquer the change of pose than 2D images.This thesis addresses to study the 3D face recognition algorithms. The main contributions of the work are as follows:1,If the data of initial 3-D point-cloud is enough , the grid control points can be used to simulate the point-cloud data which is created by B-spline surface fitting. We standardize the point-cloud data to reduce the quantity of point-cloud data to raise efficiency of our algorithm.2,Contour lines are extracted according to depth information feature. Then, features of nose and the profile subsection curvature are obtained by analyzing curvatures of contour lines.3,The characteristic of eigenvector is analyzed, and the similarity between face samples is measured using Euler distance and cross correlation function.4,The 3D face recognition system is developed using visual C++6.0 and SQL Server 2000. The contour lines of the face are found and their curvatures are computed first. Then, by analyzing curvatures the eigenvectors of face are extracted. Finally, the similarities between face samples are measured to recognize the face. Experiments have been conducted to show the feasibility of our algorithm.