Face Recognition Using Holistic Features and Linear Discriminant Analysis Simplification

Abstract

This paper proposes an alternative approach to face recognition algorithm that is based on global/holistic features of face image and simplified linear discriminant analysis (LDA). The proposed method can overcome main problems of the conventional LDA in terms of large processing time for retraining when a new class data is registered into the training data set. The holistic features of face image are proposed as dimensional reduction of raw face image. While, the simplified LDA which is the redefinition of between class scatter using constant global mean assignment is proposed to decrease time complexity of retraining process. To know the performance of the proposed method, several experiments were performed using several challenging face databases: ORL, YALE, ITS-Lab, INDIA, and FERET database. Furthermore, we compared the developed algorithm experimental results to the best traditional subspace methods such as DLDA, 2DLDA, (2D)2DLDA, 2DPCA, and (2D)22DPCA. The experimental results show that the proposed method can be solve the retraining problem of the conventional LDA indicated by requiring shorted retraining time and stable recognition rate.