TY  - JOUR
AU  - Kumar, Mathu Soothana S. 
AU  - Swami, Retna 
AU  - Karuppiah, Muneeswaran 
PY  - 2011
TI  - An Improved Face Recognition Technique Based on Modular LPCA Approach
JF  - Journal of Computer Science
VL  - 7
IS  - 12
DO  - 10.3844/jcssp.2011.1900.1907
UR  - https://thescipub.com/abstract/jcssp.2011.1900.1907
AB  - Problem statement: A face identification algorithm based on modular localized variation by Eigen Subspace technique, also called modular localized principal component analysis, is presented in this study. Approach: The face imagery was partitioned into smaller sub-divisions from a predefined neighborhood and they were ultimately fused to acquire many sets of features. Since a few of the normal facial features of an individual do not differ even when the pose and illumination may differ, the proposed method manages these variations. Results: The proposed feature selection module has significantly, enhanced the identification precision using standard face databases when compared to conservative and modular PCA techniques. Conclusion: The proposed algorithm, when related with conservative PCA algorithm and modular PCA, has enhanced recognition accuracy for face imagery with illumination, expression and pose variations.