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Proceedings Paper

Real-time compact optoelectronic neural networks for face recognition
Author(s): Bahram Javidi; Jian Li
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Paper 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.

Paper Details

Date Published: 14 June 1996
PDF: 12 pages
Proc. SPIE 2749, Photonic Component Engineering and Applications, (14 June 1996); doi: 10.1117/12.243104
Show Author Affiliations
Bahram Javidi, Univ. of Connecticut (United States)
Jian Li, Univ. of Connecticut (United States)


Published in SPIE Proceedings Vol. 2749:
Photonic Component Engineering and Applications
Andrew R. Pirich, Editor(s)

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