Face Recognition using Principle Component Analysis ( PCA ) and Linear Discriminant Analysis ( LDA )

Image Recognition is one of the computer vision applications in recent years. Commercially, security and law applications require the use of face recognition technology. Human face can be regarded as the most obvious human identifier. Apparently the face is the most visible part of human anatomy and serves as the first distinguishing factor of a human being. It helps a person to distinguish an individual from one to another. Each individual has his own uniqueness and this could be one of the most transparent and unique feature of a human being. Face recognition involves comparing an image with a database of stored faces in order to identify the individual in that input image. The images can be analyzed and faces can then be identified, before they can be recognized. There are different methods of face recognition which involve a series of steps that serve to capturing, analyzing and comparing a face to a database of stored images. This project covered comparative study of image recognition between Linear Discriminant analysis (LDA) and Principal Component Analysis (PCA). In this study, the result of PCA and LDA will be analyzed in term of its accuracy, percentage of correct recognition, time execution and database used. Index Term— PCA, LDA, feature extraction.