Research, Face Recognition Algorithm Based On Eigenface And Features

An accurate automatic individual identification of person is critical to a wide range of application domains such as access control, electronic commerce, and welfare benefits disbursement. Traditional personal identification methods such as key word, IC card etc., however, suffer from a number of drawbacks and are unable to satisfy the security requirements of our highly inter-connected information society. Biometrics refers to an individual based on his/her physiological and/or behavioral traits. Naturely it is likely an identification panacea. In recent years, it is beginning to provide very powerful tools for the problems requiring positive identification.Human face recognition is one of the most reliable biometric technology. An automatic face recognition system has two important aspects, one is face detection and location, the other is face feature extraction and recognition or identification. In the paper we introduce several common used methods of the technologies on the two aspects. Then, the dissertation focuses on conventional face recognition algorithm -Principal Component Analysis (PCA) and its further improvement in which we introduce the second-order eigenface method first and then propose a method based on multiple feature combination. The main contributions of this thesis includes:Two methods of pre-processing aiming to deal with the variability in appearance are studied. They are gray normalization and gray statistic map equilibrium. We have also compared the methods using eigenface for face recognition experiments.We have conducted recognition experiments many times to study the problem of selecting how many eigenvectors is best for recognition.Because the conventional eigenface method is sensitive to the lighting variability in appearance, the second-order eigenface method is introduced. The method discards many eigenfaces that obtained by conventional eigenface method...