Ear recognition based on force field feature extraction and convergence feature extraction

Jiajia Luo; Zhichun Mu; Yu Wang

doi:10.1117/12.806740

13 October 2008 Ear recognition based on force field feature extraction and convergence feature extraction

Jiajia Luo, Zhichun Mu, Yu Wang

Proceedings Volume 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence; 71272E (2008) https://doi.org/10.1117/12.806740
Event: Seventh International Symposium on Instrumentation and Control Technology, 2008, Beijing, China

Abstract

Ear recognition based on the force field transform is new and effective. Three different applications of the force field transform were discussed in this paper. Firstly, we discussed the problem in the process of potential wells extraction and overcame the contradiction between the continuity of the force field and the discreteness of intensity images. Secondly, an improved convergence-based ear recognition method was presented in this paper. To overcome the problem of threshold segmentation, an adaptive threshold segmentation method was used to find the threshold automatically; to reduce the computational complexity, a quick classification was realized by combining the Canny-operator and the Modified Hausdorff Distance (MHD). Finally, the algebraic property of force field was combined with Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) together to obtain feature vectors for ear recognition. We tested these applications of the force field transform on two ear databases. Experimental results show the validity and robustness of the force field transform for ear recognition.

Citation Download Citation

Jiajia Luo, Zhichun Mu, and Yu Wang "Ear recognition based on force field feature extraction and convergence feature extraction", Proc. SPIE 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence, 71272E (13 October 2008); https://doi.org/10.1117/12.806740

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