Feature Extraction and Fusion Techniques for Patch-Based Face Recognition

Face recognition is one of the most addressed pattern recognition problems in recent studies due to its importance in security applications and human computer interfaces. After decades of research in the face recognition problem, feasible technologies are becoming available. However, there is still room for improvement for challenging cases. As such, face recognition problem still attracts researchers from image processing, pattern recognition and computer vision disciplines. Although there exists other types of personal identification such as fingerprint recognition and retinal/iris scans, all these methods require the collaboration of the subject. However, face recognition differs from these systems as facial information can be acquired without collaboration or knowledge of the subject of interest. Feature extraction is a crucial issue in face recognition problem and the performance of the face recognition systems depend on the reliability of the features extracted. Previously, several dimensionality reduction methods were proposed for feature extraction in the face recognition problem. In this thesis, in addition to dimensionality reduction methods used previously for face recognition problem, we have implemented recently proposed dimensionality reduction methods on a patch-based face recognition system. Patch-based face recognition is a recent method which uses the idea of analyzing face images locally instead of using global representation, in order to reduce the effects of illumination changes and partial occlusions. Feature fusion and decision fusion are two distinct ways to make use of the extracted local features. Apart from the wellknown decision fusion methods, a novel approach for calculating weights for the weighted sum rule is proposed in this thesis. On two separate databases, we have conducted both feature fusion and decision fusion experiments and presented recognition accuracies for different dimensionality reduction and normalization methods. Improvements in recognition accuracies are shown and superiority of decision fusion over feature fusion is advocated. Especially in the more challenging AR database, we obtain significantly better results using decision fusion as compared to conventional methods and feature fusion methods.