Performance Analysis of Various Feature Extraction Techniques in Ear Biometrics

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 216)

Abstract

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.

Keywords

Feature extraction PCA LDA KPCA Verification rate False acceptance rate 

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Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSRM UniversityKattankulathurIndia
  2. 2.Ministry of Higher EducationCollege of Applied SciencesSoharOman

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