@article {10.3844/jcssp.2011.1900.1907,
article_type = {journal},
title = {An Improved Face Recognition Technique Based on Modular LPCA Approach},
author = {Kumar, Mathu Soothana S. and Swami, Retna and Karuppiah, Muneeswaran},
volume = {7},
number = {12},
year = {2011},
month = {Oct},
pages = {1900-1907},
doi = {10.3844/jcssp.2011.1900.1907},
url = {https://thescipub.com/abstract/jcssp.2011.1900.1907},
abstract = {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.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}