By Topic

A Novel Fast Face Recognition Method of Two-Dimensional Principal Component Analysis Based on BP Neural Networks

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
$33 $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)
Wenjing Han ; Coll. of Inf. & Electr. Eng., Shandong Univ. of Sci. & Technol., Qingdao ; Jing Li ; Nongliang Sun

Two-dimensional principal component analysis technique is an important and well-developed area of image recognition and to date this method has been put forward. A new face recognition method two-dimensional principal component analysis (2DPCA) based on BP neural networks, named 2DPCA-BP method, was proposed. 2DPCA was used to obtain a family of projected feature vectors, in which face image was projected into this family of projected feature vectors to get the feature matrix. BP-based neural network was used as classifier for its good learning capability. Experiment proved that 2DPCA-BP is better than 2DPCA-SVMs in velocity and its recognition accuracy is 98.246%. The CVL database showed that the system achieved excellent performance.

Published in:

Computational Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia Workshop on  (Volume:1 )

Date of Conference:

19-20 Dec. 2008