Share Email Print

Proceedings Paper

High-performance distributed memory and computing algorithm for face recognition via conformal mapping
Author(s): Dalila B. Megherbi; A. J. Boulenouar; Venkataramanan Rajagopalan
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This work deals with a new distributed face recognition technique based on transformation invariant conformal mapping. Face recognition using digital images is constrained by several factors like rotation (in and out of plane), scaling, and is usually operated under strict lighting conditions. In a previous work, through a conformal mapping process, we demonstrated the ability to 1) recognize shapes, and 2) concisely represent shape boundaries using a set of polynomial coefficients derived in the mapping process. In this work we illustrate how these previous results can be applied to face recognition. Additionally, in the approach outlined herein, a syntactic representation is formed for polygonal and non-polygonal shapes representing a given face features whose representation we desire to extract and reproduce compactly. In particular, in this paper we focus on the face outline, left eyebrow, right eyebrow, left eye, right eye, nose, curve form the lower nose to upper lips, and lips as face features. Some of these face features are grouped together and then processed in parallel in a distributed network of workstations via a Message Passing Interface running over TCP/IP. In particular, we show that a master-salve paradigm is used here to implement the proposed parallel and distributed algorithm and is base don a distributed-memory approach. Test were performed with 1,2, and three workstations. The algorithm assumes no constraints in the lighting, and the size of the window that has the face. This work deals with face representation and recognition and face segmentation is not the subject of this paper. We finally show the potential of the proposed generalized technique in its ability to handle both polygonal, non- polygonal and mixed polygonal/non-polygonal object shapes. We also show that the proposed algorithm achieves high recognition rates for rotations in the plane, translation, and scaling and is robust in noisy environments.

Paper Details

Date Published: 28 August 2001
PDF: 12 pages
Proc. SPIE 4388, Visual Information Processing X, (28 August 2001); doi: 10.1117/12.438263
Show Author Affiliations
Dalila B. Megherbi, Univ. of Massachusetts/Lowell (United States)
A. J. Boulenouar, Univ. of Massachusetts/Lowell (United States)
Venkataramanan Rajagopalan, Univ. of Massachusetts/Lowell (United States)

Published in SPIE Proceedings Vol. 4388:
Visual Information Processing X
Stephen K. Park; Zia-ur Rahman; Robert A. Schowengerdt, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?