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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)

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. 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.

Keywords

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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References

  1. 1.
    Jain, L.C., Halici, U., Hayashi, I., Lee, S.B.: Intelligent biometric techniques in fingerprint and face recognition. CRC Press, Boca Raton (1999)Google Scholar
  2. 2.
  3. 3.
  4. 4.
    Chellapa, R., et al.: Human and Machine Recognition of Faces: A Survey. UMCP CS-TR-3399 (1994)Google Scholar
  5. 5.
    Hallinan, P.L., Gordon, G.G., Yuille, A.L., Giblin, P., Mumford, D.: Two and three dimensional pattern of the face. A K Peters Ltd (1999)Google Scholar
  6. 6.
    Chua, C.S., Han, F., Ho, Y.K.: 3D Human Face Recognition Using Point Signature. In: Proc. of the 4th ICAFGR (2000)Google Scholar
  7. 7.
    Tanaka, H.T., Ikeda, M., Chiaki, H.: Curvature-based face surface recognition using spherial correlation. In: Proc. of the 3rd IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 372–377 (1998)Google Scholar
  8. 8.
    Gordon, G.G.: Face Recognition based on depth and curvature feature. In: Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 808–810 (1992)Google Scholar
  9. 9.
    Chellapa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: A survey. Proceedings of the IEEE 83(5), 705–740 (1995)CrossRefGoogle Scholar
  10. 10.
    Lee, J.C., Milios, E.: Matching range image of human faces. In: Proc. of the 3rd Int. Conf. on Computer Vision, pp. 722–726 (1990)Google Scholar
  11. 11.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  12. 12.
    Hesher, C., Srivastava, A., Erlebacher, G.: Principal Component Analysis of Range Images for Facial Recognition. In: Proc. of CISST (2002)Google Scholar
  13. 13.
    Zhao, Z.Q., Huang, D.S., Sun, B.Y.: Human face recognition based on multi-features using neural networks committee. Pattern Recognition Letters 25, 1351–1358 (2004)CrossRefGoogle Scholar
  14. 14.
    Davidson, J.L., Ritter, G.X.: A Theory of Morphological Neural Networks. SPIE 1215, 378–388 (1990)Google Scholar
  15. 15.
    Ritter, G.X., Li, D., Wilson, J.N.: Image Algebra and its Relationship to Neural Networks. SPIE 1098, 90–101 (1989)Google Scholar
  16. 16.
    Han, C.W., Park, J.I.: SA-selection-based Genetic Algorithm for the Design of Fuzzy Controller. International Journal of Control, Automation, and Systems 3(2), 236–243 (2005)Google Scholar
  17. 17.
    Peet, F.G., Sahota, T.S.: Surface Curvature as a Measure of Image Texture. IEEE Trans. PAMI 7(6), 734–738 (1985)CrossRefGoogle Scholar

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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