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.
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Annapurani, K., Malathy, C., Sadiq, A.K. (2014). Performance Analysis of Various Feature Extraction Techniques in Ear Biometrics. In: Sathiakumar, S., Awasthi, L., Masillamani, M., Sridhar, S. (eds) Proceedings of International Conference on Internet Computing and Information Communications. Advances in Intelligent Systems and Computing, vol 216. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1299-7_38
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DOI: https://doi.org/10.1007/978-81-322-1299-7_38
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