Co-occurrence matrix and its statistical features as a new approach for face recognition

Alaa ELEYAN; Hasan DEMİREL

Article Details

Co-occurrence matrix and its statistical features as a new approach for face recognition

Authors:

Alaa ELEYAN | Hasan DEMİREL

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2011 Vol.19 Issue 1, pp.97-107

Document Type:

Article

Publication Date:

12/2010

Document Language:

Turkish

Document ID (Org No.):

2000743821

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632

Abstract:

 

In this paper, a new face recognition technique is  introduced based on the gray-level co-occurrence matrix (GLCM).  GLCM  represents the distributions of the intensities and the information  about relative positions of neighboring pixels of an image.  We  proposed two methods to extract feature vectors using GLCM for face  classification.  The first method extracts the well-known Haralick  features from the GLCM, and the second method directly uses GLCM by  converting the matrix into a vector that can be used in the  classification process.  The results demonstrate that the second  method, which uses GLCM directly, is superior to the first method that  uses the feature vector containing the statistical Haralick features  in both nearest neighbor and neural networks classifiers.  The  proposed GLCM based face recognition system not only outperforms  well-known techniques such as principal component analysis and linear  discriminant analysis, but also has comparable performance with local  binary patterns and Gabor wavelets.

 

In this paper, a new face recognition technique is  introduced based on the gray-level co-occurrence matrix (GLCM).  GLCM  represents the distributions of the intensities and the information  about relative positions of neighboring pixels of an image.  We  proposed two methods to extract feature vectors using GLCM for face  classification.  The first method extracts the well-known Haralick  features from the GLCM, and the second method directly uses GLCM by  converting the matrix into a vector that can be used in the  classification process.  The results demonstrate that the second  method, which uses GLCM directly, is superior to the first method that  uses the feature vector containing the statistical Haralick features  in both nearest neighbor and neural networks classifiers.  The  proposed GLCM based face recognition system not only outperforms  well-known techniques such as principal component analysis and linear  discriminant analysis, but also has comparable performance with local  binary patterns and Gabor wavelets.

Article Details

Co-occurrence matrix and its statistical features as a new approach for face recognition

Authors: Alaa ELEYAN | Hasan DEMİREL

Publisher: TUBITAK

Journal: Turkish Journal of Electrical Engineering and Computer Science 2011 Vol.19 Issue 1, pp.97-107

Document Type: Article

Publication Date: 12/2010

Document Language: Turkish

Document ID (Org No.): 2000743821

Frequency: Bimonthly

eISSN: 1303-6203

pISSN: 1300-0632

Issues

Authors:

Alaa ELEYAN | Hasan DEMİREL

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2011 Vol.19 Issue 1, pp.97-107

Document Type:

Article

Publication Date:

12/2010

Document Language:

Turkish

Document ID (Org No.):

2000743821

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632