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

Alaa ELEYAN; Hasan DEMİREL

Makale detayları

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

Yazarlar:

Alaa ELEYAN | Hasan DEMİREL

Yayıncı:

TUBITAK

Dergi:

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

Belge Tipi:

Makale

Yayın Tarihi:

12/2010

Belge Dili:

Türkçe

Document ID (Org No.):

2000743821

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

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.

Makale detayları

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

Yazarlar: Alaa ELEYAN | Hasan DEMİREL

Yayıncı: TUBITAK

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

Belge Tipi: Makale

Yayın Tarihi: 12/2010

Belge Dili: Türkçe

Document ID (Org No.): 2000743821

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Alaa ELEYAN | Hasan DEMİREL

Yayıncı:

TUBITAK

Dergi:

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

Belge Tipi:

Makale

Yayın Tarihi:

12/2010

Belge Dili:

Türkçe

Document ID (Org No.):

2000743821

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632