Cart (Loading....) | Create Account
Close category search window
 

Genetic search for face detection and verification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Bebis, G. ; Dept. of Comput. Sci., Nevada Univ., Reno, NV, USA ; Uthiram, S. ; Georgiopoulos, M.

We investigate the application of genetic algorithms (GAs) to search for the face of a particular individual in a two-dimensional intensity image. This problem has many potential applications such as locating a person in a crowd from pictures taken by surveillance cameras. There are two steps in solving this problem: first, faces regions must be extracted from the image (face detection) and second, candidate faces must be compared against the race of interest (face verification). Without any a priori knowledge about location and size of a face in an image, every possible image location and face size must be considered, leading to a very large search space. In addition, face detection or verification invariant to lighting conditions, facial expression and pose make the search space even larger and more complex. In the paper, we propose using GAs to search the image efficiently. Specifically, we use GAs to find image sub-windows that contain the face of interest. Each sub-window is evaluated using a fitness function which contains two terms: the first term favors sub-windows containing faces while the second term favors sub-windows containing faces similar to the face of interest. Both terms have been defined using ideas from the method of “eigenfaces”. A set of increasingly complex scenes demonstrate the performance of the genetic search approach

Published in:

Information Intelligence and Systems, 1999. Proceedings. 1999 International Conference on

Date of Conference:

1999

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.