INTRODUCTION OF FACE BIOMETRIC RECOGNITION

Machine biometric recognition of human faces is a challenging problem due to the changes in the face identity and variation between images of the same face due to illumination and viewing direction. The issues are how the features are adopted to represent a face under environmental changes and how the classification is done to a new face image based on the chosen representations.


Principal Component Analysis (PCA) which also known as eigenfaces is used in this research to extract set of feature extraction of the faces. The reason that it is chosen due to its capability to extract the relevant information from high dimensional matrix [Turk, 1991]. As for the classification task, the euclidean distance and backpropagation neural network is chosen since most researchers show superior performance claimed that both methods.


A set of features from a face image is representation using the eigenvalues and eigenvectors. In order to obtain eigenvalues and eigenvectors, Jacobi’s method is used due its accuracy and robustness. As for the classification task, backpropagation algorithm is applied.