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.




1.2    Problem Statement


The ability of human to recognize of thousands of faces is remarkable. This skill is impressive even with large changes of human faces such as aging, hairstyle, and expressions. Moreover with environmental changes of lighting, distractions (glasses, face scar) and changes of human skin color make the developing the computational model of face biometric recognition is a challenging. Thus, the proposes superior methods to develop the human face biometric recognition prototype.




1.3    Objective


The fundamental objectives of this work are:
a.    To study and implement the Principal Component Analysis (PCA) in order to extract the features of face images.
b.    To study and implement the optimization technique in order to obtain eigenvalues and eigenvectors.
c.    To study and implement the backpropagation neural network for classification.
d.    To propose three (3) face biometric recognition models based on PCA and backpropagation neural network.
e.    To evaluate the performance of those models using known face database images downloaded via internet.
f.    To develop a prototype of human face biometric recognition system. 




1.4    Scope


Some limitations of the research are described below as:


a.    This research only consists grey face image with portable grey map (PGM) image format.
b.    Since unavailable of camera support, the experiments is tested with the ORL face database which downloaded from the internet.
c.    Due to the superior capability of PCA, preprocessing is not required.
d.    Due to complexity of computational and hardware requirement, only 15 persons are tested.