By Topic

A subspace approach to face detection with support vector machines

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)
Haizhou Ai ; Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China ; Lihang Ying ; Guangyou Xu

We present a subspace approach to face detection with support vector machines (SVMs). A linear SVM classifier is trained as a filter to produce a subspace in which a non-linear SVM classifier with Gaussian kernel is trained for face detection. This makes training easier and results in a very efficient face detection algorithm. Experimental results demonstrate their promising performance compared with some well-known existing detectors.

Published in:

Pattern Recognition, 2002. Proceedings. 16th International Conference on  (Volume:1 )

Date of Conference:

2002