Face Recognition using Localized Facial Features

ABSTRACT: Face is a complex multi-dimensional structure and needs good computing biometric techniques for recognition. The aim of this study is to understand the role of each localized facial feature component in face recognition system and treat it as a one-dimensional recognition problem. In this context, face recognition is performed by using Principal Component Analysis (PCA) method. Face images are stored in a face database that encodes best variation among face images. Instead of recognizing human characteristic from full face data, identifying the facial feature components seperately might be alternative classification method to get successful recognition performance face is defined by eigenface which are eigenvectors of the set of face components. Each face feature is extracted by using automatic/manual segmentation techniques to have facial features such as left eyes, right eyes, nostrils and mouth. Finally, each segmented facial feature can be one classifier and combination of each may help to form multi-classifier problem to achieve improved recognition results. Proposed face recognizion system, which is using localized facial features along with global face, improves PCA-based face system by average of 4.5 %. Keywords: Face Recognition, Principal Component Analysis, Segmentation Techniques, Multi-Classifier Problem. …………………………………………………………………………………………………………………………