Hybridization of Facial Features and Use of Multi Modal Information for 3D Face Recognition
Subject Areas : Image, Speech and Signal Processing
1 - Computer Technology, Priyadarshini college of Engineering,nagpur
Keywords:
Abstract :
1. Adler, A., M.E.J.I.T.o.S. Schuckers, Man,, and P.B. Cybernetics, Comparing human and automatic face recognition performance. 2007. 37(5): p. 1248-1255; Available from: https://sci2s.ugr.es/keel/pdf/specific/articulo/Comparing%20Human%20and%20Automatic%20Face%20Recognition%20Performance.pdf.
2. Lu, X. and A.K. Jain. Automatic feature extraction for multiview 3D face recognition. in 7th International Conference on Automatic Face and Gesture Recognition (FGR06). 2006. IEEE.
3. Belghini, N., A. Zarghili, and J.J.S.I.I.J.C.A.S.E.D.E.S. Kharroubi, 3D face recognition using Gaussian Hermite moments. 2012. 1: p. 1-4; Available from: https://www.researchgate.net/profile/Naouar_Belghini/publication/269105595_3D_Face_Recognition_using_Gaussian_Hermite_Moments/links/5480e9cc0cf20f081e726b20.pdf.
4. Gervei, O., et al., 3D face recognition using modified PCA methods. 2010. 39; Available from: https://pdfs.semanticscholar.org/670f/30de92077cb242e76375374530ebd300cda9.pdf.
5. Gottumukkal, R. and V.K.J.P.R.L. Asari, An improved face recognition technique based on modular PCA approach. 2004. 25(4): p. 429-436; Available from: https://www.sciencedirect.com/science/article/pii/S0167865503002654.
6. Cavalcanti, G.D., T.I. Ren, and J.F.J.E.S.w.A. Pereira, Weighted modular image principal component analysis for face recognition. 2013. 40(12): p. 4971-4977; Available from: https://www.sciencedirect.com/science/article/pii/S095741741300153X.
7. Tan, K. and S.J.N. Chen, Adaptively weighted sub-pattern PCA for face recognition. 2005. 64: p. 505-511; Available from: https://www.sciencedirect.com/science/article/pii/S0925231204005600.
8. Kumar, A.P., S. Das, and V. Kamakoti. Face recognition using weighted modular principle component analysis. in International Conference on Neural Information Processing. 2004. Springer.
9. Berretti, S., et al., 3D face recognition using isogeodesic stripes. 2010. 32(12): p. 2162-2177; Available from: https://ieeexplore.ieee.org/abstract/document/5432188/.
10. Han, X., et al., Face recognition in the presence of expressions. 2012. 5(05): p. 321; Available from: https://file.scirp.org/pdf/JSEA20120500002_68626424.pdf.
11. Smeets, D., et al. Fusion of an isometric deformation modeling approach using spectral decomposition and a region-based approach using ICP for expression-invariant 3D face recognition. in 2010 20th International Conference on Pattern Recognition. 2010. IEEE.
12. Wang, C., et al., A hybrid method to build a canonical face depth map. 2011. 5(5).
13. Xu, C., et al., Automatic 3D face recognition from depth and intensity Gabor features. 2009. 42(9): p. 1895-1905; Available from: https://www.sciencedirect.com/science/article/pii/S0031320309000089.
14. Assadi, A. and A. Behrad. A new method for human face recognition using texture and depth information. in 10th Symposium on Neural Network Applications in Electrical Engineering. 2010. IEEE.
15. Hasan, M.H.M., et al., 3-D Face Recognition Using Improved 3D Mixed Transform. 2012; Available from: http://www.cscjournals.org/download/issuearchive/IJBB/Volume6/IJBB_V6_I1.pdf#page=19.
16. Thakare, N.M. and V. Thakare, A Robust and Novel Framework for Subdivision of Face Image Based on the Concept of Golden Rule. Available from: https://pdfs.semanticscholar.org/776c/757c96b42070ec58de5f7be2062ed82811b5.pdf.
17. Meisner, G.B., Beauty in the Human Face and the Golden Ratio. 2014; Available from: https://www.goldennumber.net/beauty/.
18. Research, C.f.B.a.S., Note on CASIA 3D Face Database. 2005; Available from: http://www.cbsr.ia.ac.cn/english/3DFace%20Databases.asp.