Abstract
The depth information in the face represents personal features in detail. In particular, the surface curvatures extracted from the face contain the most important personal facial information. The principal component analysis using the surface curvature reduces the data dimensions with less degradation of original information, and the proposed 3D face recognition algorithm collaborated into them. The recognition for the eigenface referred from the maximum and minimum curvatures is performed. To classify the faces, the cascade architectures of fuzzy neural networks, which can guarantee a high recognition rate as well as parsimonious knowledge base, are considered. Experimental results on a 46 person data set of 3D images demonstrate the effectiveness of the proposed method.
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Lee, YH., Han, CW. (2006). 3D Facial Recognition Using Eigenface and Cascade Fuzzy Neural Networks: Normalized Facial Image Approach. In: Grigoriev, D., Harrison, J., Hirsch, E.A. (eds) Computer Science – Theory and Applications. CSR 2006. Lecture Notes in Computer Science, vol 3967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11753728_46
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DOI: https://doi.org/10.1007/11753728_46
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