Principal component net analysis for face recognition

Authors:
Lianghua He;Die Hu;Changjun Jiang
Affiliations:
School of Electronics and Information Engineering, Tongji University, Shanghai, China;School of Information Science and Engineering, Fudan University, Shanghai, China;School of Electronics and Information Engineering, Tongji University, Shanghai, China
Venue:
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Year:
2006

Citing 6
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Abstract

In this paper, a new feature extraction called principal component net analysis (PCNA) is developed for face recognition. It looks a face image upon as two orthogonal modes: row channel and column channel and extracts Principal Components (PCs) for each channel. Because it does not need to transform an image into a vector beforehand, much more spacial discrimination information is reserved than traditional PCA, ICA etc. At the same time, because the two channels have different physical meaning, its extracted PCs can be understood easier than 2DPCA. Series of experiments were performed to test its performance on three main face image databases: JAFFE, ORL and FERET. The recognition rate of PCNA was the highest (PCNA, PCA and 2DPCA) in all experiments.