Improved face recognition using extended modular principal component analysis

Authors:
Changhan Park;Inho Paek;Joonki Paik
Affiliations:
Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea;Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea;Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea
Venue:
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Year:
2006

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Abstract

In this paper, we present an improved face recognition algorithm using extended modular principal component analysis (PCA). The proposed method, when compared with a regular PCA-based algorithm, has significantly improved recognition rate with large variations in pose, lighting direction, and facial expression. The face images are divided into multiple, smaller blocks based on the Gaussian model and we use the PCA approach to these combined blocks for obtaining two eyes, nose, mouth, and glabella. Priority for merging blocks is decided by using fuzzy logic. Some of the local facial features do not vary with pose, lighting direction, and facial expression. The proposed technique is robust against these variations.