Three Dimensional Face Recognition For Template Protection Technology

Identity authentication is one of the most important basis of information securitysystem. Compared with passwords and smart cards, biometric identification is unforget-table and not easy to lose, which affords users higher level security protection. Thus,biometric identification has become the trend for identity authentication.Nowadays, the security vulnerability of existing biometric identification technologyhas drawn more and more attention of researchers. Most of the security threats are causedby traditional direct storage templates.he technology of biometric template protection hasemerged in recent years, which turns the traditional template into a secret style to store.As such, there will be no information leakage of the original templates during identityauthentication. However, there still exist many theoretical and application problems.While biometric template protection technology ensures the security of templates, itbrings great challenges to existing identification technologies. As the template informa-tion is not directly stored, most existing 3D face recognition algorithms cannot work dueto the restrictions for registration, feature extraction and feature matching.In this thesis, we adopt the template protection technology to 3D face recognitionunder restrictions. The contributions of our thesis are as follows:We have proposed a 3D face preprocessing method for template protection. Ourmethod could preprocess information efficiently, accurately, and automatically. The ef-fectiveness of our preprocessing method is shown through the simulation results.We have proposed two typically feature extraction algorithms for template protec-tion. The first algorithm is based on local statistic features, containing the histogramvalues of point number and energy that are extracted from the divided horizontal layer.The second one is based on global features using space integration. We resample the 3Dface model and integrate the gray value, curve rate and depth value into a new feature, anduse pattern recognition to train the feature. The two algorithms are proved to be highlyeffective for recognition.