Face Recognition System

Abstract

Eigenfaces approach for face recognition is implemented as our final project. Face recognition has been an active area of research with numerous applications since late 1980s. Eigenface approach is one of the earliest appearance-based face recognition methods, which was developed by M. Turk and A. Pentland [1] in 1991. This method utilizes the idea of the Principal Component Analysis and decomposes face images into a small set of characteristic feature images called Eigenfaces. Recognition is performed by projecting a new face onto a low dimensional linear “face space” defined by the eigenfaces, followed by computing the distance between the resultant position in the face space and those of known face classes. A number of experiments were done to evaluate the performance of the face recognition system we have developed. The results demonstrate that the Eigenfaces approach is quite sensitive to head/face orientation scale and illumination. At the end of the report, a couple of ways are suggested to improve the recognition rate. The report is organized as follows: the first part provides an overview of face detection algorithms; the second part states the implementation of the Haar Based approach for face detection; Part III focuses on overview of Face Recognition algorithms and Part IV focuses on the implementation part of face recognition.