Range Facial Recognition with the Aid of Eigenface and Morphological Neural Networks

  • Chang-Wook Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)


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. These surface curvature and eigenface, which reduce the data dimensions with less degradation of original information, are collaborated into the proposed 3D face recognition algorithm. The principal components represent the local facial characteristics without loss for the information. Recognition for the eigenface referred from the maximum and minimum curvatures is performed. To classify the faces, the max plus algebra based neural networks (morphological neural networks) optimized by hybrid genetic algorithm are considered. Experimental results on a 46 person data set of 3D images demonstrate the effectiveness of the proposed method.


Face Recognition Face Image Memetic Algorithm Hybrid Genetic Algorithm Stereo Match 
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 2008

Authors and Affiliations

  • Chang-Wook Han
    • 1
  1. 1.Department of Electrical EngineeringDong-Eui UniversityBusanSouth Korea

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