Multi-pose ear recognition using locally linear embedding and nearest feature line

Zhaoxia Xie; Zhichun Mu; Dehui Sun; Dunli Hu

doi:10.1117/12.806729

13 October 2008 Multi-pose ear recognition using locally linear embedding and nearest feature line

Zhaoxia Xie, Zhichun Mu, Dehui Sun, Dunli Hu

Proceedings Volume 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence; 71272A (2008) https://doi.org/10.1117/12.806729
Event: Seventh International Symposium on Instrumentation and Control Technology, 2008, Beijing, China

Abstract

Multi-pose ear recognition is a challenging problem of ear recognition technology. In this paper, the method based on locally linear embedding (LLE) and nearest feature line (NFL) is proposed by analyzing the disadvantages of most 2D ear recognition methods currently when dealing with poses variation and the shortcomings of the nearest neighbor (NN) classifier. The LLE algorithm is used to extract the ear feature, and then the NFL classifier is applied to classify ear images under varying poses. By contrast, experimental results show that the method based on LLE and NFL can obviously improve the recognition performance, which demonstrates the validity of this method.

Citation Download Citation

Zhaoxia Xie, Zhichun Mu, Dehui Sun, and Dunli Hu "Multi-pose ear recognition using locally linear embedding and nearest feature line", Proc. SPIE 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence, 71272A (13 October 2008); https://doi.org/10.1117/12.806729

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available