Sharif Digital Repository

Biometrics has been long known to recognize persons based on their physical and behavioral characteristics. Face recognition (FR) is one of such biometrics that has received a considerable attention in recent years both from the industry and research communities. As the boosting framework has shown good performance in face recognition, it has been adopted in this work. This thesis deals with pattern recognition methods such as linear discriminant analysis (LDA) and machine learning approaches such as boosting which are integrated to overcome the technical limitation of existing FR methods. However, LDA-based methods often suffer from the so-called “small-sample-size” (SSS) problem arising...