Improving face recognition using combination of global and local features

This paper presents the face recognition using discrete orthogonal moment that is Krawtchouk moments (KMs) on combined features of global and local face images. KMs are considered due to their ability to localize face image according to Region of Interest (ROI) unlike other moments which generally capture the global features. To obtain the global face image, both parameters of KMs are set equal at 0.5 while the parameters for local face image are set unequal. The selection of orders and parameters of KMs determines the ROI. The experiments carried out utilized the database face images from Olivetti research laboratory (ORL) consisting of 40 subjects of 10 non-similar images each [1]. Each face image differs in terms of position, rotation, scale and expression, with and without glasses. Euclidean square distance or Nearest Neighbour (NN) is used as the classifier in the recognition stage. From the experiments, the combined features of global and local faces images using KMs showed improved classification accuracy.

[1]  C. R. Henson.  Conclusion , 1969 .

[2]  Karim Faez,et al.  A hybrid learning RBF neural network for human face recognition with pseudo Zernike moment invariant , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[3]  A. Saradha,et al.  A Hybrid Feature Extraction Approach for Face Recognition Systems , 2005 .

[4]  Raveendran Paramesran,et al.  Image analysis by Krawtchouk moments , 2003, IEEE Trans. Image Process..

[5]  Paramesran Raveendran,et al.  Krawtchouk moments as a new set of discrete orthogonal moments for image reconstruction , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[6]  Andrew Beng Jin Teoh,et al.  An efficient method for human face recognition using wavelet transform and Zernike moments , 2004, Proceedings. International Conference on Computer Graphics, Imaging and Visualization, 2004. CGIV 2004..

[7]  Ashraf A. Kassim,et al.  Character recognition using statistical moments , 1999, Image Vis. Comput..