3D Facial Recognition Using Eigenface and Cascade Fuzzy Neural Networks: Normalized Facial Image Approach

  • Yeung-Hak Lee
  • Chang-Wook Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3967)


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.


Face Recognition Recognition Rate Face Image Memetic Algorithm Fuzzy Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yeung-Hak Lee
    • 1
  • Chang-Wook Han
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceYeungnam UniversityGyongbukSouth Korea

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