Digital Image Processing

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Cornea Recognition Using a Cepstral Approach and SVM


Affiliations
1 Computers and Systems Department, The Electronic Research Institute, Egypt
2 Department of Electrical and Electronic Engineering, University of Liverpool, United Kingdom
3 Department of Electronics and Electrical Communications, Menoufia University, Egypt
4 Menoufia University, Menouf, Egypt
     

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In this paper, a new technique for feature extraction from corneal images is presented which can be applied for corneal pattern recognition. Most of the previous methods are based on segmentation of the corneal images which are restricted to certain planes. In this paper, a proposed method is applied on corneal images which have two main phases. Firstly, the 2-D images are lexicographically ordered to 1-D signals, and then the Mel Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients are extracted from these 1-D signals or from their transforms. Secondly, the SVM is used to match the extracted features in the testing phase to those of the training phase. Experimental results show that the recognition rate for features extracted from Discrete Sine Transform DST and Discrete Cosine Transform (DCT) achieve better performance compared to other cases. The method in this paper is limited to feature extraction for pattern recognition and the automatic diagnosis case is left for future work.


Keywords

Corneal Images, Pattern Recognition, Mel Frequency Cepstral Coefficients (MFCCs), Polynomial Coefficients, Support Vector Machine (SVM), Discrete Cosine Transforms (DCT), Discrete Sine Transforms (DST), Discrete Wavelet Transforms (DWT).
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  • Cornea Recognition Using a Cepstral Approach and SVM

Abstract Views: 215  |  PDF Views: 2

Authors

Nahed Tawfik
Computers and Systems Department, The Electronic Research Institute, Egypt
Mahmoud Fakhr
Computers and Systems Department, The Electronic Research Institute, Egypt
Al-Nuaimy Waleed
Department of Electrical and Electronic Engineering, University of Liverpool, United Kingdom
Moawad I. Dessouky
Department of Electronics and Electrical Communications, Menoufia University, Egypt
Fathi E. Abd EI-Samie
Menoufia University, Menouf, Egypt

Abstract


In this paper, a new technique for feature extraction from corneal images is presented which can be applied for corneal pattern recognition. Most of the previous methods are based on segmentation of the corneal images which are restricted to certain planes. In this paper, a proposed method is applied on corneal images which have two main phases. Firstly, the 2-D images are lexicographically ordered to 1-D signals, and then the Mel Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients are extracted from these 1-D signals or from their transforms. Secondly, the SVM is used to match the extracted features in the testing phase to those of the training phase. Experimental results show that the recognition rate for features extracted from Discrete Sine Transform DST and Discrete Cosine Transform (DCT) achieve better performance compared to other cases. The method in this paper is limited to feature extraction for pattern recognition and the automatic diagnosis case is left for future work.


Keywords


Corneal Images, Pattern Recognition, Mel Frequency Cepstral Coefficients (MFCCs), Polynomial Coefficients, Support Vector Machine (SVM), Discrete Cosine Transforms (DCT), Discrete Sine Transforms (DST), Discrete Wavelet Transforms (DWT).