research-article

Electronic Door Access Control using MyAccess Two-Factor Authentication Scheme featuring Near-Field Communication and Eigenface-based Face Recognition using Principal Component Analysis

Authors:

Delina Beh Mei Yin,

Mohamad Izzuddin Kamal,

Nurul Sharaz Azmanuddin,

Siti Haryani Shaikh Ali,

Abu Talib Othman,

Rita Zaharah Wan ChikAuthors Info & Claims

IMCOM '16: Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication

Article No.: 1, Pages 1 - 8

https://doi.org/10.1145/2857546.2857548

Published: 04 January 2016 Publication History

Abstract

By implementing authentication, it can ensure that system's resources are not obtained fraudulently by illegitimate users. Currently, most commonly used for door access controls is single-factor authentication (SFA). Problem arises when SFA is not able to identify the legitimate user and user identity accurately. Thus, SFA can be easily breached and compromised by various types of threats. In order to overcome the problem, a two-factor authentication (TFA) namely MyAccess has been proposed to escalate the security level for door access control. MyAccess uses the combination factor of something you have and something you are; i.e., the Near Field Communication (NFC) technology is something you have and face recognition is something you are. For the first authentication, the credential token can be personalised by using NFC sticker for users to attach on any personal belonging (e.g. Mobile phone, wallet, etc.). Whilst eigenface-based face recognition using Principal Component Analysis (PCA) takes place in the second authentication to serve a high security level for door access control. MyAccess outperforms its strength by easing the tension of memorability because it does not require the user to remember any combination of password or Personal Identification Number (PIN). Several testing has been carried out to evaluate the level of acceptance and accuracy on the input faces to be verified for the user identity under a few controlled-environments such as variations of illumination and appearance of obstacles.

References

[1]

Bai, Q., and Zheng, Y., 2011. Study on the access control model. In: IEEE Proceedings of 2011 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, 830--834. http://

[2]

Investigation, I. 2008. Selecting a development approach, 1--10

[3]

Hwang, I., and Baek, J., 2007. Wireless access monitoring and control system based on digital door lock. IEEE Transactions on Consumer Electronics, 53(4), 1724--1730.

Digital Library

[4]

Lee, M. H., Yao, C. Y., and Liu, H. C. 2011. Passive Tag for Multi-carrier RFID Systems, 872--876. In IEEE 17th International Conference on Parallel and Distributed Systems (ICPADS), 872--876.

Digital Library

[5]

Nasution, S. M., Husni, E. M., and Wuryandari, A. I. 2012. Prototype of train ticketing application using Near Field Communication (NFC) technology on Android device. In International Conference on System Engineering and Technology (ICSET), 1--6.

[6]

Verma, G. K., & Tripathi, P. 2010. A Digital Security System with Door Lock System Using RFID Technology. International Journal of Computer Applications (IJCA), 5, 11.

[7]

Jain, A. K., & Kumar, A. 2010. Biometrics of Next Generation: An Overview. Proceedings Second Generation Biometrics' Springer.

[8]

Juels, A. 2004. Minimalist Cryptography for Low-Cost RFID Tags. Security and Cryptography for Networks, 149---164.

Digital Library

[9]

Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289--1305.

Digital Library

[10]

Madlmayr, G., Langer, J., Kantner, C., & Scharinger, J. 2008. NFC Devices: Security and Privacy. 2008 Third International Conference on Availability, Reliability and Security, 642--647.

Digital Library

[11]

Ferro, E., & Potorti, F. 2005. Bluetooth and Wi-Fi wireless protocols: a survey and a comparison, Wireless Communications, IEEE, 12, 1, 12--26.

Digital Library

[12]

Vaculík, J., Kolarovszki, P., and Tengler, J., 2013. Possibility of RFID in Conditions of Postal Operator.

[13]

Pathak, V., Patwari, D. N., Khanale, P. B., Tamboli, N. M., Pathak, V. M., and Machine, S. V., 2011. Study and Comparison of Different Face Recognition Algorithms, 1(1). Retrieved from http://www.bioinfo.in/contents.php?id=162

[14]

Roberts, C., 2007. Biometric attack vectors and defences. Computers & Security, 26, 1, pp.14--25.

Digital Library

[15]

Tomás, F. B., and Tomas, B. 2010. Computer Vision and Face Recognition.

[16]

Michele, C. (2013, August 5). How does facial recognition work? Retrieved from http://www.synel.co.uk/blog/bid/95787/How-does-facial-recognition-work

[17]

De Carrera, P. F., & Marques, I. 2010. Face recognition algorithms. Master's thesis in Computer Science, Universidad Euskal Herriko.

[18]

Turk, M., Pentland, A., Recognition, E., & Neuroscience, C. 1991. Eigenfaces for Face Detection / Recognition, 3, 1, 1--11.

[19]

Hiremath, V., and Mayakar, A. 2009. Face recognition using Eigenface approach. In IDT workshop on interesting results in computer science and engineering, Sweden.

[20]

Holand, S. M. 2008. Principal components analysis (PCA). Retrieved from http://strata.uga.edu/software/pdf/pcaTutorial.pdf.

[21]

He, X., Yan, S., Hu, Y. and Zhang, H.J., 2003. Learning a locality preserving subspace for visual recognition. In Ninth IEEE International Conference on Computer Vision Proceedings. 385--392.

Digital Library

[22]

He, X., Yan, S., Hu, Y., Niyogi, P., and Zhang, H. J. 2005. Face recognition using Laplacianfaces. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 3, 328--340.

Digital Library

[23]

He, X., and Partha Niyogi, 2003. Locality Preserving Projections, Advances in Neural Information Processing Systems, Vancouver, Canada.

[24]

Cai, X. F., Wen, G. H., Wei, J., and Li, J. 2011. Enhanced supervised locality preserving projections for face recognition. In IEEE International Conference on Machine Learning and Cybernetics (ICMLC), 4, 1762--1766.

[25]

Madhulakshmi, A. W. A. 2013. Face Recognition Using Featured Histogram. International Journal of Emerging Technology and Advanced Engineering, 3, 8.

[26]

Alliance, S. C. 2002. Contactless technology for secure physical access: Technology and standards choices. Smart Card Alliance Research Report.

[27]

Peris-Lopez, P., Hernandez-Castro, J. C., Estevez-Tapiador, J. M., and Ribagorda, A. 2009. 2 Attacking RFID Systems. Security in RFID and Sensor Networks, 29.

[28]

Rashid, R., Mahalin, N. H., Sarijari, M. A., & Abdul Aziz, A. 2008. Security system using biometric technology: Design and implementation of Voice Recognition System (VRS). In IEEE International Conference on Computer and Communication Engineering (ICCCE), 898--902.

[29]

Faundez-Zanuy, M. 2004. A door-opening system using a low-cost fingerprint scanner and a PC. IEEE Aerospace and Electronic Systems Magazine, 19, 8, 23--26.

[30]

Prabhakar, S., Pankanti, S., & Jain, A. K. 2003. Biometric recognition: Security and privacy concerns. IEEE Security & Privacy, 2, 33--42.

Digital Library

[31]

Curran, K., Millar, A., & Mc Garvey, C. 2012. Near field communication. International Journal of Electrical and Computer Engineering (IJECE), 2, 3, 371--382.

[32]

Coskun, V., Ozdenizci, B., & Ok, K. 2013. A survey on near field communication (NFC) technology. Wireless personal communications, 71, 3, 2259--2294.

Digital Library

[33]

Lades, M. 1997. Face recognition: eigenface, elastic matching, and neural nets. Proceedings of the IEEE, 85, 9, 1423--1435.

[34]

Telagarapu, P., Suresh, G., Suman, J. V., and Lalitha, N. V. 2012. A Comprehensive Methodological Analysis of Eigen Face Recognition and Reconstruction. International Journal of Computer Theory and Engineering, 4, 1, 81.

Cited By

Chuldun ZMarlena DPriambodo DArizal (2022)Multilayers Physical Authentication and NoSQL PRESENT algorithm for Data Center2022 FORTEI-International Conference on Electrical Engineering (FORTEI-ICEE)10.1109/FORTEI-ICEE57243.2022.9972986(26-31)Online publication date: 11-Oct-2022
https://doi.org/10.1109/FORTEI-ICEE57243.2022.9972986
Klieme ETrenz PPaeschke DTietz CMeinel C(2021)DoorCollect: Towards a Smart Door Handle for User Identification based on a Data Collection System for unsupervised Long-Term Experiments2021 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC53001.2021.9631517(1-7)Online publication date: 5-Sep-2021
https://doi.org/10.1109/ISCC53001.2021.9631517
Chishti MKing CBanerjee A(2021)Exploring Half-Duplex Communication of NFC Read/Write Mode for Secure Multi-Factor AuthenticationIEEE Access10.1109/ACCESS.2020.30487119(6344-6357)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2020.3048711
Show More Cited By

Index Terms

Electronic Door Access Control using MyAccess Two-Factor Authentication Scheme featuring Near-Field Communication and Eigenface-based Face Recognition using Principal Component Analysis
1. Security and privacy
  1. Systems security
    1. Operating systems security

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cover image ACM Conferences

IMCOM '16: Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication

January 2016

658 pages

ISBN:9781450341424

DOI:10.1145/2857546

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing
SKKU: SUNGKYUNKWAN UNIVERSITY

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Publication History

Published: 04 January 2016

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Research-article
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Refereed limited

Funding Sources

Malaysian Institute of Information Technology, Universiti Kuala Lumpur

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IMCOM '16

Sponsor:

SIGAPP
SKKU

IMCOM '16: The 10th International Conference on Ubiquitous Information Management and Communication

January 4 - 6, 2016

Danang, Viet Nam

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Overall Acceptance Rate 213 of 621 submissions, 34%

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Cited By

Chuldun ZMarlena DPriambodo DArizal (2022)Multilayers Physical Authentication and NoSQL PRESENT algorithm for Data Center2022 FORTEI-International Conference on Electrical Engineering (FORTEI-ICEE)10.1109/FORTEI-ICEE57243.2022.9972986(26-31)Online publication date: 11-Oct-2022
https://doi.org/10.1109/FORTEI-ICEE57243.2022.9972986
Klieme ETrenz PPaeschke DTietz CMeinel C(2021)DoorCollect: Towards a Smart Door Handle for User Identification based on a Data Collection System for unsupervised Long-Term Experiments2021 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC53001.2021.9631517(1-7)Online publication date: 5-Sep-2021
https://doi.org/10.1109/ISCC53001.2021.9631517
Chishti MKing CBanerjee A(2021)Exploring Half-Duplex Communication of NFC Read/Write Mode for Secure Multi-Factor AuthenticationIEEE Access10.1109/ACCESS.2020.30487119(6344-6357)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2020.3048711
Mei Yin DMukhlas AWan Chik RTalib Othman AOmar S(2018)A Proposed Approach for Biometric-Based Authentication Using of Face and Facial Expression Recognition2018 IEEE 3rd International Conference on Communication and Information Systems (ICCIS)10.1109/ICOMIS.2018.8644974(28-33)Online publication date: Dec-2018
https://doi.org/10.1109/ICOMIS.2018.8644974
Bullee JMontoya LJunger MHartel P(2018)Physical location of smart key activators – a building security penetration testJournal of Corporate Real Estate10.1108/JCRE-05-2017-001420:2(138-151)Online publication date: 14-May-2018
https://doi.org/10.1108/JCRE-05-2017-0014
Jain ALalwani SJain SKarandikar V(2018)IoT-Based Smart Doorbell Using Raspberry PiInternational Conference on Advanced Computing Networking and Informatics10.1007/978-981-13-2673-8_20(175-181)Online publication date: 28-Nov-2018
https://doi.org/10.1007/978-981-13-2673-8_20
Yin DOmar STalip BMuklas ANorain NOthman A(2017)Fusion of face recognition and facial expression detection for authenticationProceedings of the 11th International Conference on Ubiquitous Information Management and Communication10.1145/3022227.3022247(1-8)Online publication date: 5-Jan-2017
https://dl.acm.org/doi/10.1145/3022227.3022247

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