A low cost compact optoelectronics neural networks for face recognition

Author

Javidi, Bahrm

Author_Institution

Dept. of Electr. & Syst. Eng., Connecticut Univ., Storrs, CT, USA

Volume

1

fYear

1994

fDate

31 Oct-3 Nov 1994

Firstpage

8

Abstract

Summary form only given. In this paper, we describe a nonlinear filter based optoelectronics neural networks associated with a supervised perceptron learning algorithm for real-time face recognition. The system is a two-layer neural networks. The first layer is implemented using a nonlinear joint transform correlator (JTC)34 and the second layer is implemented electronically because of the small number of the hidden layer neurons. The system is trained with a sequence of input facial images and is able to classify an input face in real-time

Keywords

face recognition; face recognition; hidden layer neurons; input face; input facial images; low cost compact optoelectronics neural networks; nonlinear filter based optoelectronics neural networks; nonlinear joint transform correlator; real-time face recognition; supervised perceptron learning algorithm; trained; two-layer neural networks; Costs; Face recognition; Integrated optics; Lighting; Neural networks; Neurons; Nonlinear optics; Optical filters; Optical sensors; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Lasers and Electro-Optics Society Annual Meeting, 1994. LEOS '94 Conference Proceedings. IEEE

Conference_Location

Boston, MA

Print_ISBN

0-7803-1470-0

Type

conf

DOI

10.1109/LEOS.1994.586864

Filename

586864