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 applied to the Common Matrix Approach (CMA) based face recognition system. For each of these feature extraction methods, recognition rates are obtained in the AR face database, ORL database and Yale database. Experimental results indicate that recognition rates obtained by the 2D mel-cepstrum method is superior to the recognition rates obtained using Fourier LDA approach and raw image matrices. This indicates that 2D mel-cepstral analysis can be used in image feature extraction problems. © 2010 IEEE
One limitation of vector-based LDA and its matrix-based extension is that they cannot deal with hete...
Abstract—In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) ...
Abstract—In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) ...
A feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced. The concept of...
In this paper, a feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced....
An image feature extraction method based on the twodimensional (2-D) mel cepstrum is introduced. The...
An image feature extraction method based on two-dimensional (2D)Mellin cepstrum is introduced. The c...
A feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced. The concept of...
Cataloged from PDF version of article.In this article, an image feature extraction method based on t...
Solving speech recognition problems requires an adequate feature extraction technique to transform t...
(LDA) are two important feature extraction methods and have been widely applied in a variety of area...
This paper develops a new image feature extraction and recognition method coined two-dimensional lin...
Although 2DLDA algorithm obtains higher recognition accuracy, a vital unresolved problem of 2DLDA is...
In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is develo...
By formulating two-dimensional principle component analysis (2DPCA) as a mathematical form different...
One limitation of vector-based LDA and its matrix-based extension is that they cannot deal with hete...
Abstract—In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) ...
Abstract—In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) ...
A feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced. The concept of...
In this paper, a feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced....
An image feature extraction method based on the twodimensional (2-D) mel cepstrum is introduced. The...
An image feature extraction method based on two-dimensional (2D)Mellin cepstrum is introduced. The c...
A feature extraction method based on two-dimensional (2D) mel-cepstrum is introduced. The concept of...
Cataloged from PDF version of article.In this article, an image feature extraction method based on t...
Solving speech recognition problems requires an adequate feature extraction technique to transform t...
(LDA) are two important feature extraction methods and have been widely applied in a variety of area...
This paper develops a new image feature extraction and recognition method coined two-dimensional lin...
Although 2DLDA algorithm obtains higher recognition accuracy, a vital unresolved problem of 2DLDA is...
In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is develo...
By formulating two-dimensional principle component analysis (2DPCA) as a mathematical form different...
One limitation of vector-based LDA and its matrix-based extension is that they cannot deal with hete...
Abstract—In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) ...
Abstract—In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) ...