Share Email Print
cover

Proceedings Paper

Texture analysis for ear recognition using local feature descriptor and transform filter
Author(s): Jun Feng; Zhichun Mu
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Ear recognition is a kind of the novel representative subjects in the field of non-disturbance biometrics authentication and is becoming received wide attention in academic research. In this paper, the ear recognition problem based on texture analysis is discussed. A novel local wavelet binary pattern descriptor combining local binary pattern descriptor with wavelet transform filter is presented. And an ear recognition approach based on local wavelet binary pattern descriptor and support vector machines classification is proposed, which is tested on USTB ear image set. The experiment results show that the ear recognition scheme using local feature descriptor and transform filter is effective and promising. The performance of support vector machines classifier is better than that of K Nearest Neighbor classifier. The best combination occurs under the Chi square distance and 'reverse biorthogonal 3.1' wavelet, and the 96.86% cross- validation recognition rate is obtained.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74962P (30 October 2009); doi: 10.1117/12.832749
Show Author Affiliations
Jun Feng, Shijiazhuang Institute of Railway (China)
Univ. of Science and Technology Beijing (China)
Zhichun Mu, Univ. of Science and Technology Beijing (China)


Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

© SPIE. Terms of Use
Back to Top