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.
Author: I Gede Pasek Suta
Wijaya, Keiichi Uchimura, Gou Koutaki
Journal Code: jptkomputergg120107
