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.
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He, L., Hu, D., Jiang, C. (2006). Principal Component Net Analysis for Face Recognition. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_70
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DOI: https://doi.org/10.1007/11925231_70
Publisher Name: Springer, Berlin, Heidelberg
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