An Improved Face Recognition Technique Based on Modular LPCA Approach
Mathu Soothana S. Kumar, Retna Swami and Muneeswaran Karuppiah
DOI : 10.3844/jcssp.2011.1900.1907
Journal of Computer Science
Volume 7, Issue 12
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.
© 2011 Mathu Soothana S. Kumar, Retna Swami and Muneeswaran Karuppiah. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.