Abstract
In this paper, we present a new scheme called Common Image method for face recognition. Our method has a couple of advantages over the conventional face recognition algorithms; one is that it can deal with the Small Sample Size(SSS) problem in LDA, and the other one is that it can achieve a better performance than traditional PCA by seeking the optimal projection vectors from image covariance matrix in a recognition task. As opposed to traditional PCA-based methods and LDA-based methods which employ Euclidean distance, Common Image methods adopted Assemble Matrix Distance(AMD) and IMage Euclidean Distance(IMED), by which the overall recognition rate could be improved. To test the recognition performance, a series of experiments were performed on CMU PIE, YaleB, and FERET face databases. The test results with these databases show that our Common Image method performs better than Discriminative Common Vector and 2DPCA-based methods.
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© 2006 Springer-Verlag Berlin Heidelberg
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Seo, H.J., Park, Y.K., Kim, J.K. (2006). Common Image Method(Null Space + 2DPCAs) for Face Recognition. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_64
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DOI: https://doi.org/10.1007/11864349_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44630-9
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