Shape Prediction Model-Based Face Recognition Under Variant Illumination

Face recognition has been the research focus in computer graphics, computer vision and pattern recognition as its wide range of applications and scientific value. There are many face recognition techniques have been proposed and shown significant promise, and many commercial systems are available for various applications under well-controlled environment in return for the long time research effort, but robust face recognition is still difficult as many unresolved challenges, such as illumination and pose variation problems, expression problem and so on. This thesis is based on the foundation of researching on face recognition in my laboratory and the projects that I have taken part in, the main contributions of it are as following:1. Provides a review of face recognition literature. Although there are some good surveys of face recognition to be published in the past few years, we think it is necessary to give a new overview of face recognition from different viewpoints because there are many new algorithms and technologies to be presented very year. This survey describes the face recognition technology from viewpoints of historical development, state-of-the-arts in the world, the classical algorithms, performance evaluation and key issues in face recognition.2. Measures quantificationally the three-dimensional shape and two-dimensional surface reflectance contributions to face images recognition. Based on the BJPU-3D large-scale Chinese face database and the correspondence between these faces by though mesh resampling, we have altered the three-dimensional shape and two-dimensional surface reflectance of the original 3D faces by making use of the average face, then we have implemented a series of experiments to measure the contributions of three-dimensional shape and two-dimensional surface reflectance to face image recognition under this change pattern. From the experiment results based on the Eigenface algorithm, we concludes that the two types of information are both important for face recognition but the 3D shape is much more important than the 2D surface reflectance,this conclusion gives good suggestions and clues to the design of face recognition algorithms in future.3. Proposes a face recognition method by synthesizing virtual face images based on face shape prediction model by through the deep analysis of the illumination problem. We train the support vector regression (SVR) model through examples of two-dimensional (2D) and...