Face recognition using the neural tree network

Joseph Wilder; S. Juth; Augustine Tsai; X. Y. Zhang

doi:10.1117/12.172539

1 February 1994 Face recognition using the neural tree network

Joseph Wilder, S. Juth, Augustine Tsai, X. Y. Zhang

Proceedings Volume 2093, Substance Identification Analytics; (1994) https://doi.org/10.1117/12.172539
Event: Substance Identification Technologies, 1993, Innsbruck, Austria

Abstract

A face recognition system has been developed and demonstrated at the Rutgers University Center for Computer Aids for Industrial Productivity. The system uses a preliminary data reduction step. gray scale projections, and a fast transform technique to greatly reduce the computational complexity of the problem and, consequently, the cost of high-speed implementation. The decision function is a few, extremely cost-effective neural network, the Mammone/Sankar Neural Tree Network. This network can be trained and re-trained rapidly on face image data and the system has built-in facilities for acquiring and editing a large data base of face images. Recognition rates higher than 90% were achieved on data sets containing up to 269 subjects. More importantly, it performed well on subjects with and without their glasses, under a wide range of changes in facial expressions, and under a variety of small tilts, translations and rotations.

Citation Download Citation

Joseph Wilder, S. Juth, Augustine Tsai, and X. Y. Zhang "Face recognition using the neural tree network", Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); https://doi.org/10.1117/12.172539

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available