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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 34, Iss. 20 — Jul. 10, 1995
  • pp: 3950–3962

Optical implementation of neural networks for face recognition by the use of nonlinear joint transform correlators

Bahram Javidi, Jian Li, and Qing Tang  »View Author Affiliations


Applied Optics, Vol. 34, Issue 20, pp. 3950-3962 (1995)
http://dx.doi.org/10.1364/AO.34.003950


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Abstract

We describe a nonlinear joint transform correlator-based two-layer neural network that uses a supervised learning algorithm for real-time face recognition. The system is trained with a sequence of facial images and is able to classify an input face image in real time. Computer simulations and optical experimental results are presented. The processor can be manufactured into a compact low-cost optoelectronic system. The use of the nonlinear joint transform correlator provides good noise robustness and good image discrimination.

© 1995 Optical Society of America

History
Original Manuscript: May 31, 1994
Revised Manuscript: November 2, 1994
Published: July 10, 1995

Citation
Bahram Javidi, Jian Li, and Qing Tang, "Optical implementation of neural networks for face recognition by the use of nonlinear joint transform correlators," Appl. Opt. 34, 3950-3962 (1995)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-34-20-3950


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References

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