By Topic

Ear recognition method based on fusion features of global and local features

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

2 Author(s)
Hai-Jun Zhang ; Dept. of Autom., Shenyang Inst. of Aeronaut. Eng., Shenyang ; Zhi-Chun Mu

In the paper, we propose a new method for ear recognition. Firstly, we extract global features using kernel principal component analysis (KPCA) technique and extract local features using independent component analysis (ICA) technique. Then we establish a correlation criterion function between two groups of feature vectors, extract their canonical correlation features according to this criterion, and finally form effective discriminant vectors for recognition. For validation of our method, we have tested our method on the USTB ear database by using linear support vector machine. Meanwhile, we have compared performance of our method with that of KPCA-based and ICA-based methods. The experiment results show the performance of our method is superior to those of other methods.

Published in:

Wavelet Analysis and Pattern Recognition, 2008. ICWAPR '08. International Conference on  (Volume:1 )

Date of Conference:

30-31 Aug. 2008