Research On Automatic Face Recognition Algorithm Based On Statistical Learning

Automatic face recognition is a typical pattern analysis, understanding and classification problem, which is closely related to many disciplines such as pattern recognition, image processing, computer vision, statistical learning, and cognitive Psychology etc. The in-depth study and final settlement of AFR can greatly promote the maturity and development of these disciplines. While as one of the main research areas in Biometrics, face recognition and authentication techniques are believed having a great deal of potential applications in national security and public safety. Though face recognition has achieved great progress in the last 40 years, it is still a great challenge to build an automatic, high performance, high robust system for face recognition, due to the influence of illumination, pose and expression etc.In recent years, pattern recognition methods based on statistical learning have attached a great deal of attention which have achieved great success in AFR and greatly improved the speed and accuracy of AFR system. The representative algorithms include Boosting, SVM and Bayesian learning. This dissertation is just directed at the application of statistical learning algorithms in the various stages of AFR. The main work and innovation of this dissertation includes:1. Through extensive investigation and research, provided a thorough survey of the AFR history and the state-of-the-artAt first, this dissertation provided an overview of the history and the development status of AFR. Then, some main algorithms in face recognition are introduced, especially those based on static face images. In addition, the research on video-baed face recognition is also introduce, which has attached much attention in recent years. In the last, we survey the main public face databases and the performance evaluations protocols, based on which we also analyze the challeges and technical trends in AFR fields. 2. Against the existing problem of using AdaBoost for feature selection in face detection, proposed a face detection algorithms based on cost-sensitive AdaBoostCurrently, the main face detection method is the one based on AdaBoost algorithm proposed by Viola and Jones in 2001. The main disadvantage of the algorithm when training classifiers is that the two types of misclassification errors (having a face undetected and having a false alarm) are treated equally. Because in the real applications, the existing and emerging of faces is a small probability event, the cost caused by having a face undetectd is larger than that of having a false alarm. In this thesis, we propose a new AdaBoost called Cost-Sentitive AdaBoost, which sets the cost of false negative is larger than that of false positive and then seeks to minimize the total misclassification cost. The experimental results show that the proposed method can achieve better learning performance and improve the face detection rate.3. Against the existing asymmetry problems of using AdaBoost for feature selection in face recognition, proposed a new face recognition algorithm based on AsymBoost and Fisher linear discriminant analysisThis thesis analyzes in detail the asymmetries in face recognition when using AdaBoost for feature selection, such as the uneven distribution of the positive and negative samples and learning goal asymmetry. Then, we propose to use the asymmetric AdaBoost (AsymBoost) to address the asymmetries in face recognition, and adopt Fisher linear discriminant analysis (FLDA) to optimize the weights of the selected weak classifiers to maximize the separability between the data of different classes. The experimental results show that the performance of the final classifier trained by AsymBoost and FLDA is improved. 4. Studied the application of Bayesian learning in face recognition, proposed a robust pose-invariant face recognition based on LBP and Bayesian probabilistic modelThis thesis studies the influence of pose variation on the face recognition algorithms based on LBP, then proposed to Bayesian probabilistic model to model the pose variation to make the LBP face recognition algorithm be more robust to pose variation. The experimental results demonstrate that the proposed method achieved higher recognition rates in a wider range of pose changes compared to the original LBP face recognition method.