Research Article Open Access

An Improved Face Recognition Technique Based on Modular LPCA Approach

Mathu Soothana S. Kumar, Retna Swami and Muneeswaran Karuppiah

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 of Computer Science
Volume 7 No. 12, 2011, 1900-1907

DOI: https://doi.org/10.3844/jcssp.2011.1900.1907

Submitted On: 13 August 2011 Published On: 22 October 2011

How to Cite: Kumar, M. S. S., Swami, R. & Karuppiah, M. (2011). An Improved Face Recognition Technique Based on Modular LPCA Approach. Journal of Computer Science, 7(12), 1900-1907. https://doi.org/10.3844/jcssp.2011.1900.1907

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Keywords

  • Face recognition
  • feature extraction
  • Pose invariance
  • illumination invariance
  • feature vector
  • partial occlusion
  • precise class
  • recognition accuracy