Face Identification by Real-Time Connectionist System

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 217)

Abstract

This document provides an approach to biometrics analysis which consists in the location and identification of faces in real time, making the concept a safe alternative to Web sites based on the paradigm of user and password. Numerous techniques are available to implement face recognition including the principal component analysis (PCA), neural networks, and geometric approach to the problem considering the shapes of the face representing a collection of values. The study and application of these processes originated the development of a security architecture supported by the comparison of images captured from a webcam using methodology of PCA, and the Hausdorff algorithm of distance as similarity measures between a general model of the registered user and the objects (faces) stored in the database, the result is a web authentication system with main emphasis on efficiency and application of neural networks.

Keywords

Neural networks eigenfaces Hausdorff distance Face Recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Goldstein, A.J., Harmon, L.D., Lesk, A.B.: Identification of human faces. Proc. IEEE 59, 748–760 (1971)CrossRefGoogle Scholar
  2. 2.
    Intel, Intel Open Source Computer Vision Library, v2.4.1 (2006), http://sourceforge.net/projects/opencvlibrary/
  3. 3.
    Intel, EmguCV Envoltorio de la biblioteca OpenCV, v2.4.0 (2012), http://www.emgu.com
  4. 4.
    Kerin, M.A., Stonham, T.J.: Face recognition using a digital neural network with self-organizing capabilities. In: Proc. 10th Conf. on Pattern Recognition (1990)Google Scholar
  5. 5.
    Kirby, M., Sirovich, L.: Application of the Karhunen-Loeve procedure for the characterization of human faces. In: IEEE PAMI, vol. 12 (1990)Google Scholar
  6. 6.
    Lin, K.-H., Lam, K.-M., Siu, W.-C.: Spatially eigen-weighted Hausdorff distances for human face recognition. Polytechnic University, Hong Kong (2002)Google Scholar
  7. 7.
    Manjunath, B.S., Chellappa, R., Malsburg, C.: A feature based approach to face recognition. Trans. of IEEE, 373–378 (1992)Google Scholar
  8. 8.
    Dimitri, P.: Eigenface-based facial recognition (Diciembre 2002)Google Scholar
  9. 9.
    Rowley, H., Baluja, S., Kanade, T.: Neural network face detection, San Francisco, CA (1996)Google Scholar
  10. 10.
    Smith, L.I.: A tutorial on principal components analysis (February 2002), http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf (accessed on April 27, 2012)
  11. 11.
    Terrillon, J., David, M., Akamatsu, S.: Automatic detection of human faces in natural scene images by use of a skin color model, Nara, Japan (1998)Google Scholar
  12. 12.
    Turk, M., Pentland, A.: Eigenfaces for recognition (1991a), http://www.cs.ucsb.edu/mturk/Papers/jcn.pdf (accessed on April 27, 2012)
  13. 13.
    Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision (2004)Google Scholar
  14. 14.
    Yuille, A.L., Cohen, D.S., Hallinan, P.W.: Feature extraction from faces using deformable templates. In: Proc. of CVPR (1989)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.University of SalamancaSalamancaSpain

Personalised recommendations