An Improved Face Recognition Technique Based on Modular LPCA Approach | Science Publications

Journal of Computer Science

Retna Swami¹  Muneeswaran Karuppiah¹  Mathu S.S. Kumar¹ 

关键词: Face recognition;    feature extraction;    Pose invariance;    illumination invariance;    feature vector;    partial occlusion;    precise class;    recognition accuracy;   

DOI  :  10.3844/jcssp.2011.1900.1907

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

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.

【 授权许可】

Unknown