Hybridization of Facial Features and Use of Multi Modal Information for 3D Face Recognition
محورهای موضوعی : Image, Speech and Signal Processing
1 - Computer Technology, Priyadarshini college of Engineering,nagpur
کلید واژه: Hybridization, Feature Extraction, Depth Map, 3D Face Recognition,
چکیده مقاله :
Despite of achieving good performance in controlled environment, the conventional 3D face recognition systems still encounter problems in handling the large variations in lighting conditions, facial expression and head pose The humans use the hybrid approach to recognize faces and therefore in this proposed method the human face recognition ability is incorporated by combining global and local information to develop a robust face recognition system.In this papers it is proposed that hybridization of global and local facial features and combination of 2D and 3D modality helps in improving performance of face recognition system. The main issue of existing face recognition systems is the high false accept rate which is not desirable when security is the main concern. Most of the existing face recognition techniques overcome these problems with some constraints. However, the proposed methodology has achieved better results and handled all the three issues successfully. Also the use of 2.5D images (Depth Map) and dimensionality reduced data (Eigen faces) has shown that the system is computationally reasonable.
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.
