References
- Abhishek, K., Roshan, S., Kumar, P., & Ranjan, R. (2013). A comprehensive study on multifactor authentication schemes. In Advances in computing and information technology, Meghanathan N., Nagamalai D., Chaki N. (Eds.), (pp. 561–568). Springer Berlin Heidelberg.
- Adler, A. (2008). Biometric system security. In Handbook of biometrics Jain, Anil K., Flynn, Patrick, Ross, Arun A (Eds.), (pp. 381–402). Springer US.
- Battalglia, F., Iannizzotto, G., & Bello, L. L. (2014). A biometric authentication system based on face recognition and RFID tags. Mondo Digitale. http://mondodigitale.aicanet.net/2014-1/augmented_reality_e_biometrics/04_LOBELLO.pdf
- Cai, J., Chen, J., & Liang, X. (2015, January). Single-sample face recognition based on intra-class differences in a variation model. Sensors, 15(1), 1071–1087. https://doi.org/10.3390/s150101071
- Chengeta, K., & Viriri, S. (2018, March). A survey on facial recognition based on local directional and local binary patterns. In 2018 conference on information communications technology and society (ICTAS). IEEE.
- European consumers ready to use biometrics for securing payments. (2016). Visa Inc. Retrieved 2020 August, 21, from https://www.visa.co.uk/about-visa/newsroom/press-releases.1478239.html
- Hou, Y.-C. (2003, July). Visual cryptography for color images. Pattern Recognition, 36(7), 1619–1629. https://doi.org/10.1016/S0031-3203(02)00258-3
- Huang, G. B., Mattar, M., Berg, T., & Learned-Miller, E. (n.d.). Labeled faces in the wild: A database for studying face recognition in unconstrained environments. University of Massachusetts. http://vis-www.cs.umass.edu/lfw/
- Ibrahim, D. R., Abdullah, R., Teh, J. S., & Alsalibi, B. (2019). Authentication for ID cards based on colour visual cryptography and facial recognition. In Proceedings of the 3rd international conference on cryptography, security and privacy - ICCSP ‘19. Kuala Lumpur, Malaysia: ACM Press.
- Ibrahim, D. R., Tamimi, A. A., & Abdalla, A. M. (2017, May). Performance analysis of biometric recognition modalities. In 2017 8th international conference on information technology (ICIT). IEEE.
- Judith, I. D., Mary, G. J. J., & Susanna, M. M. (2016, February). Three factor biometric authentication for spiraling of security. In 2016 international conference on emerging trends in engineering, technology and science (ICETETS). IEEE.
- Kumar, M., Jindal, M. K., & Sharma, R. K. (2011, November). k-nearest neighbor based offline handwritten gurmukhi character recognition. In 2011 international conference on image information processing. IEEE.
- Lin, W.-H., Wu, B.-H., & Huang, Q.-H. (2018, April). A face-recognition approach based on secret sharing for user authentication in public-transportation security. In 2018 IEEE international conference on applied system invention (ICASI). IEEE.
- Martinez, A. M., & Benavente, R. (n.d.). The AR face database. Ohio State University. http://www2.ece.ohio-state.edu/˜aleix/ARdatabase.html.
- Mirjalili, S. (2015, May). Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing & Applications, 27(4), 1053–1073. https://doi.org/10.1007/s00521-015-1920-1
- Mohammed, A. J., & Yassin, A. A. (2019, September). Efficient and flexible multi-factor authentication protocol based on fuzzy extractor of administrator’s fingerprint and smart mobile device. Cryptography, 3(3), 24. https://doi.org/10.3390/cryptography3030024
- Mwema, J., Kimwele, M., & Kimani, S. (2015, February). A simple review of biometric template protection schemes used in preventing adversary attacks on biometric fingerprint templates. International Journal of Computer Trends and Technology, 20(1), 12–18. https://doi.org/10.14445/22312803/IJCTT-V20P103
- Nanni, L., Brahnam, S., & Lumini, A. (2012, October). A simple method for improving local binary patterns by considering non-uniform patterns. Pattern Recognition, 45(10), 3844–3852. https://doi.org/10.1016/j.patcog.2012.04.007
- Nanni, L., & Lumini, A. (2008, November). Local binary patterns for a hybrid fingerprint matcher. Pattern Recognition, 41(11), 3461–3466. https://doi.org/10.1016/j.patcog.2008.05.013
- Naor, M., & Shamir, A. (1995). Visual cryptography. In Advances in cryptology — EUROCRYPT’94, Alfredo De Santis (Ed.), (pp. 1–12). Springer Berlin Heidelberg.
- Ometov, A., Bezzateev, S., Mäkitalo, N., Andreev, S., Mikkonen, T., & Koucheryavy, Y. (2018, January). Multi-factor authentication: A survey. Cryptography, 2(1), 1. https://doi.org/10.3390/cryptography2010001
- Phillips, P., Moon, H., Rizvi, S., & Rauss, P. (2000). The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(10), 1090–1104. https://doi.org/10.1109/34.879790
- Phillips, P. J., Beveridge, J. R., Draper, B. A., Givens, G., O’Toole, A. J., Bolme, D., Dunlop, J., Lui, Y. M., Sahibzada, H., & Weimer, S. (2012, March). The good, the bad, and the ugly face challenge problem. Image and Vision Computing, 30(3), 177–185. https://doi.org/10.1016/j.imavis.2012.01.004
- Sakaue, F., & Shakunaga, T. (2005). Combination of projectional and locational decompositions for robust face recognition. In Lecture notes in computer science, Zhao W., Gong S., Tang X (Eds.), (pp. 407–421). Springer Berlin Heidelberg.
- Sakaue, F., & Shakunaga, T. (2006). Gaussian decomposition for robust face recognition. In Computer vision – ACCV 2006, Narayanan P.J., Nayar S.K., Shum HY (Eds.), (pp. 110–119). Springer Berlin Heidelberg.
- Sandhya, M., & Prasad, M. V. N. K. (2016, December). Biometric template protection: A systematic literature review of approaches and modalities. In Signal processing for security technologies, Jiang R., Al-maadeed S., Bouridane A., Crookes P., Beghdadi A (Eds.), (pp. 323–370). Springer International Publishing.
- Sangaiah, A. K., Hosseinabadi, A. A. R., Shareh, M. B., Rad, S. Y. B., Zolfagharian, A., & Chilamkurti, N. (2020, January). IoT resource allocation and optimization based on heuristic algorithm. Sensors, 20(2), 539. https://doi.org/10.3390/s20020539
- Sangaiah, A. K., Medhane, D. V., Bian, G.-B., Ghoneim, A., Alrashoud, M., & Hossain, M. S. (2020, May). Energy-aware green adversary model for cyberphysical security in industrial system. IEEE Transactions on Industrial Informatics, 16(5), 3322–3329. https://doi.org/10.1109/TII.2019.2953289
- Sangaiah, A. K., Medhane, D. V., Han, T., Hossain, M. S., & Muhammad, G. (2019, July). Enforcing position-based confidentiality with machine learning paradigm through mobile edge computing in real-time industrial informatics. IEEE Transactions on Industrial Informatics, 15(7), 4189–4196. https://doi.org/10.1109/TII.2019.2898174
- Singh, G., & Chhabra, I. (2018). Genetic algorithm implementation to optimize the hybridization of feature extraction and metaheuristic classifiers. In Hybrid metaheuristics for image analysis, Bhattacharyya S. (Ed.), (pp. 49–86). Springer International Publishing.
- Steam Guard Mobile Authenticator. (2017). Valve Corporation. Retrieved 2020 August 21, from https://support .steampowered.com/kb article.php?ref=8625-WRAH–9030
- Suryadevara, S., Naaz, R., Kapoor, S., & Sharma, A. (2011, September). Visual cryptography improvises the security of tongue as a biometric in banking system. In 2011 2nd international conference on computer and communication technology (ICCCT-2011). IEEE.
- Venukumar, V., & Pathari, V. (2016, September). Multi-factor authentication using threshold cryptography. In 2016 international conference on advances in computing, communications and informatics (ICACCI). Jaipur, India: IEEE.
- Vinay, A., Shekhar, V. S., Manjunath, N., Murthy, K. N. B., & Natarajan, S. (2017, September). Expediting automated face recognition using the novel ORB2-IPR framework. In Proceedings of international conference on cognition and recognition, D. S. Guru, T. Vasudev, H.K. Chethan, Y.H. Sharath Kumar (Eds.), (pp. 223–232). Springer.