Mel- and Mellin-cepstral feature extraction algorithms for face recognition
1526 - 1534
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Please cite this item using this persistent URLhttp://hdl.handle.net/11693/21788
In this article, an image feature extraction method based on two-dimensional (2D) Mellin cepstrum is introduced. The concept of one-dimensional (1D) mel-cepstrum that is widely used in speech recognition is extended to two-dimensions using both the ordinary 2D Fourier transform and the Mellin transform. The resultant feature matrices are applied to two different classifiers such as common matrix approach and support vector machine to test the performance of the mel-cepstrum- and Mellin-cepstrum-based features. The AR face image database, ORL database, Yale database and FRGC database are used in experimental studies, which indicate that recognition rates obtained by the 2D mel-cepstrum-based method are superior to that obtained using 2D principal component analysis, 2D Fourier-Mellin transform and ordinary image matrix-based face recognition in both classifiers. Experimental results indicate that 2D cepstral analysis can also be used in other image feature extraction problems. © The Author 2010. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.
- Research Paper 7144
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Çakir, S.; ÇEtin, A.E. (2010)A feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced. The concept of one-dimensional (1D) mel-cepstrum which is widely used in speech recognition is extended to 2D in this article. Feature ...
Çakir, S.; Enis Çetin, A. (2010)An image feature extraction method based on the twodimensional (2-D) mel cepstrum is introduced. The concept of onedimensional mel cepstrum, which is widely used in speech recognition, is extended to 2-D in this article. ...
Çakir, S.; Çetin, A.E. (2010)In this paper, a feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced. Feature matrices resulting from the 2D mel-cepstrum, Fourier LDA approach and original image matrices are individually ...