Performance Analysis of Various Feature Extraction Techniques in Ear Biometrics
Many feature extraction techniques are available to extract the features of ear. Here in this paper, we have concentrated on analyzing the best feature extraction method. We have analyzed linear and nonlinear feature extraction methods like Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), and Kernel Principal Component Analysis (KPCA). Also we have combined LDA and PCA methods, so that the best properties of the two methods are taken. For experimentation, we have used the ear images obtained from publicly available sources. The experimental results have showed that the combination of LDA and PCA gives good performance in both verification rate and false acceptance rate compared to the other techniques individually used.
KeywordsFeature extraction PCA LDA KPCA Verification rate False acceptance rate
- 1.Iannarelli, A.: Ear Identification. Forensic Identification Series. Paramount Publishing Company, Fremont, California (1989)Google Scholar
- 2.Ramesh, K.P., Rao, K.N.: Pattern extraction methods for ear biometrics - a survey. In: World Congress on Nature & Biologically Inspired Computing, (2009). NaBIC 2009, Coimbatore, India. ISBN: 978-1-4244-5053-4 Google Scholar
- 3.Burger, M., Burger, W.: Ear biometrics. In: Jain A., Bolle R., S. (eds.) Pankanti Biometrics-Personal Identification in Networked Society, Kluwer Academic Publishers, 1999 Google Scholar
- 4.Choras, M.: Image feature extraction methods for ear biometrics–a survey. In: Proceedings of computer information systems and industrial management applications, pp. 261–265, Minneapolis (2007). ISBN: 0-7695-2894-5Google Scholar
- 5.Scholkopf, B., Smola, A.J., Miller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural computation 10(5), 1299–1319, Massachusetts Institute of Technology, (1998) Google Scholar