Face Recognition Based on Principal Component Analysis and Linear Discriminant Analysis

Abstract

This paper presents the two novel face detection techniques which are based on the singular vector decomposition (SVD) and Eigen value decomposition (EVD). Here, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods are applied to detect the features of faces which act as the principle component for the face recognition problem. The human face is full of information but working with all the information is time consuming and less efficient. It is better get unique and important information and discards other useless information in order to make system efficient. Principal component analysis is applied to find the aspects of face which are important for identification. Eigenvectors and eigen faces are calculated from the initial face image set. New faces are projected onto the space expanded by eigen faces and represented by weighted sum of the eigen faces. These weights are used to identify the faces. To reduce the time complexity and Euclidean distance in face space, here two techniques Singular Value Decomposition and Eigen-value decomposition are utilized. Simulation results have been presented to illustrates the effectiveness of the proposed face detection techniques. Here, face detection is performed using Principal Component Analysis and Linear Discriminant Analysis methods with Singular Value and Eigen-value decomposition. Experiments on face database shows the effectiveness of our proposed algorithm and results compared to PCA using Eigen Value Decomposition(EVD) shows that the proposed scheme gives comparatively better results than previous methods in terms of reduced time complexity without effecting its accuracy. Based on the results presented it is concluded that the Principal Component Analysis using EVD leads to superior results compared to that of other reported methods. From the result it is also noticed that the time complexity of the proposed method reduces considerable which leads to real time applicability of the proposed method.