Multimodal Biometric Login System Using Face and Signature

1. A.AlAbdulwahid, N.Clarke, I.Stengel ,S.Furnell, and C.Reich, ‘‘Continuous and transparent multimodal authentication: Reviewing the
state of th eart,’’ Cluster Comput.,vol.19,no.1,pp.455–474,Mar.2016.
2. P. Arias-Cabarcos, C. Krupitzer, and C. Becker, ‘‘A survey on adaptive authentication,’’ACMComput.Surv.,vol.52,no.4,pp.1–30,Sep.
2019.
3. S. Ayeswarya and J.Norman,‘‘A survey ond ifferent continuous authentication systems,’’Int.J.Biometrics,vol.11,no.1,p.67,2019.
4. C.Lisetti and C.LeRouge,“Affective computing intele-home health:Design science possibilities in recognition of adoption and diffusion
issues,”inProc.37thIEEEHawaii Int.Conf.Syst.Sci.,Hawaii,USA,Jan.2004,pp.348–363.
5. Y. Pang, Y. Yuan, and X. Li, “Iterative subspace analysis based on feature line distance,” IEEE Trans. Image Process. , vol. 18, no. 4,
pp. 903–907, Apr.2009.
6. P. J. Phillips, J. R. Beveridge, B. A. Draper, G. Givens, A. J. O’Toole, D. S.Bolme, J. Dunlop, Y. M. Lui, H. Sahibzada, and S. Weimer,
“An introduction to the good, the bad, &the ugly face recognition challenge problem,” in 2011IEEE International Conference on
Automatic Face & Gesture Recognition and Workshops (FG). IEEE,2011,pp.346–353.
7. R. Ptucha and A. Savakis, “LGE-KSVD: Flexible Dictionary Learning for Optimized Sparse Representation Classification,” in 2013
IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2013,pp.854–861.
8. M. H. Siddiqi, A. M. Khan, T. C. Chung, and S. Lee, “A precise
recognitionmodelforhumanfacialexpressionrecognitionsystem,”inProc.26thIEEECan.Conf.Elect.Comput.Eng.,2013.
9. M. Singh, R. Singh, and A. Ross, ‘‘A comprehensive overview of biometric fusion,’’Inf.Fusion,vol.52,pp.187–205,Dec.2019.
10. Y.Taigman, M.Yang, M.Ranzato, and L.Wolf, “Deepface: Closing the gap to human-level performance in face verification,” in 2014 IEEE
Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2014,pp.1701–1708.