Abstract
A method of face recognition using a weighted modular principle component analysis (WMPCA) is presented in this paper. The proposed methodology has a better recognition rate, when compared with conventional PCA, for faces with large variations in expression and illumination. The face is divided into horizontal sub-regions such as forehead, eyes, nose and mouth. Then each of them are separately analyzed using PCA. The final decision is taken based on a weighted sum of errors obtained from each sub-region.A method is proposed, to calculate these weights, which is based on the assumption that different regions in a face vary at different rates with expression, pose and illumination.
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© 2004 Springer-Verlag Berlin Heidelberg
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Kumar, A.P., Das, S., Kamakoti, V. (2004). Face Recognition Using Weighted Modular Principle Component Analysis. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_55
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DOI: https://doi.org/10.1007/978-3-540-30499-9_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
Online ISBN: 978-3-540-30499-9
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