Mel-cepstral feature extraction methods for image representation

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Abstract
An image feature extraction method based on the two-dimensional (2-D) mel cepstrum is introduced. The concept of one-dimensional mel cepstrum, which is widely used in speech recognition, is extended to 2-D in this article. The feature matrix resulting from the 2-D mel-cepstral analysis are applied to the support-vector-machine classifier with multi-class support to test the performance of the mel-cepstrum feature matrix. The AR, ORL, and Yale face databases are used in experimental studies, which indicate that recognition rates obtained by the 2-D mel-cepstrum method are superior to the recognition rates obtained using 2-D principal-component analysis and ordinary image-matrix-based face recognition. Experimental results show that 2-D mel-cepstral analysis can also be used in other image feature extraction problems.

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