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.
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Park, C., Paek, I., Paik, J. (2006). Improved Face Recognition Using Extended Modular Principal Component Analysis. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_60
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DOI: https://doi.org/10.1007/11919476_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48628-2
Online ISBN: 978-3-540-48631-2
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