Appearance Global and Local Structure Fusion for Face Image Recognition

@inproceedings{Muntasa2011AppearanceGA,
  title={Appearance Global and Local Structure Fusion for Face Image Recognition},
  author={Arif Muntasa and Indah Agustien Sirajudin and Mauridhi Hery Purnomo},
  year={2011}
}
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… CONTINUE READING
Highly Cited
This paper has 25 citations. REVIEW CITATIONS

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-10 of 16 references

Face recognition using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2005
View 5 Excerpts
Highly Influenced

Automatic Eigenface Selection For Face Recognition

A Muntasa, M Hariadi, MH. Purnomo
The 9 Seminar on Intelligent Technology and Its Applications. Surabaya • 2008
View 2 Excerpts

Orthogonal Laplacianfaces for Face Recognition

IEEE Transactions on Image Processing • 2006
View 2 Excerpts

Sistem Pengenalan Wajah Pada Subruang Orthogonal Dengan Menggunakan Laplacianfaces Terdekomposisi Qr, Seminar nasional

N Made, R. Sulaiman
Pasca Sarjana VI. ITS Surabaya • 2006
View 1 Excerpt

Orthogonal neighborhood preserving projections

Fifth IEEE International Conference on Data Mining (ICDM'05) • 2005
View 2 Excerpts

Similar Papers

Loading similar papers…