Appearance Global and Local Structure Fusion for Face Image Recognition

Principal component analysis (PCA) and linear descriminant analysis (LDA) are an extraction method based on appearance with the global structure features. The global structure features have a weakness; that is the local structure features can not be characterized . Whereas locality preserving projection (LPP) and orthogonal laplacianfaces (OLF) methods are an appearance extraction with the local structure features, but the global structure features are ignored. For both the global and the local structure features are very important. Feature extraction by using the global or the local structures is not enough. In this research, it is proposed to fuse the global and the local structure features based on appearance. The extraction results of PCA and LDA methods are fused to the extraction results of LPP. Modelling results were tested on the Olivetty Research Laboratory database face images. The experimental results show that our proposed method has achieved higher recognation rate than PCA, LDA, LPP and OLF Methods .

[1]  S. A. Khayam.  The Discrete Cosine Transform ( DCT ) : Theory and Application 1 , 2003 .

[2]  M.Kom,et al.  SISTEM PENGENALAN WAJAH PADA SUBRUANG ORTHOGONAL DENGAN MENGGUNAKAN LAPLACIANFACES TERDEKOMPOSISI QR , 2009 .

[3]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[4]  Murinto Murinto.  PENGENALAN WAJAH MANUSIA DENGAN METODE PRINCIPLE COMPONENT ANALYSIS (PCA) , 2007 .

[5]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[6]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Rama Chellappa,et al.  Discriminant analysis of principal components for face recognition , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[8]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[9]  Yousef Saad,et al.  Orthogonal neighborhood preserving projections , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[10]  Bruce A. Draper,et al.  ANALYSIS OF PCA-BASED AND FISHER DISCRIMINANT-BASED IMAGE RECOGNITION ALGORITHMS , 2000 .

[11]  Jiawei Han,et al.  Orthogonal Laplacianfaces for Face Recognition , 2006, IEEE Transactions on Image Processing.

[12]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Aziz Umit Batur,et al.  Illumination-robust face recognition , 2003 .

[14]  Anil K. Jain,et al.  Combining classifiers for face recognition , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[15]  Xiaofei He,et al.  Using Graph Model for Face Analysis , 2005 .