OCA: An intelligent model for improving security breach of biometric based authentication systems

Sharma, Sandeep Kumar; Kumar, Anil; Ashtagi, Rashmi; Jain, Rekha

doi:10.47974/JDMSC-1765

Open Access Research Article

OCA: An intelligent model for improving security breach of biometric based authentication systems

^*Sandeep Kumar SharmaCorresponding authorsandeep.sharma@jaipur.manipal.eduDepartment of Computer and Communication EngineeringManipal University JaipurJaipur, Rajasthan, IndiaView full profile → , Anil Kumaranil.vashu@gmail.comDepartment of Computer Science and EngineeringPoornima Institute of Engineering and Technology, JaipurJaipur, Rajasthan, IndiaView full profile → , Rashmi Ashtagirashmiashtagi@gmail.comDepartment of Computer EngineeringBibwewadiVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Rekha Jainrekha.jain@jaipur.manipal.eduDepartment of Computer ApplicationManipal University JaipurJaipur, Rajasthan, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 1415–1425Vol. 26Issue 5August 2023DOI: 10.47974/JDMSC-1765 Crossmark XML

Published Online:: 09 Sep 2023
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-1765
Pages:: 1415–1425

Abstract

In the recent era, biometrics is one of the most used authentication systems. Generally, biometric systems are based on imaging technology, which works on scanned images. This system processes the image and compares it with the database and decides authenticity of user. Authentication and faster response are the key objectives of all biometric systems, but they suffered from a serious issue i.e., security breach. In this article, a Cellular Automata based method is introduced, which processes the image and identify possible security breaches. The proposed method analysis all the pixels of the image and maps a rule, which processes the image and generates a gray image by defining all the possible edges of the image objects. The gray image contains only binary values, which may reduce the chance of breach and decrease the comparison time of gray image with database. In this paper, the proposed method is experimented on the open-source dataset and the results are validated using authentication parameters.

Keywords

Biometric Authentication Cellular automata Edge Transition function

Subject Classifications

Primary 03B70Secondary 03D10

References

[1] Gaurav Meena & Sarika Choudhary. Biometric authentication in internet of things: A conceptual view, Journal of Statistics and Management Systems, 22:4, 643-652 (2019).
[2] Kumar, A., & Sharma, S. K. Information cryptography using cellular automata and digital image processing. Journal of Discrete Mathematical Sciences and Cryptography, 25(4), 1105-1111 (2022). https://doi.org/doi.org/10.1080/09720529.2022.2072437.
[3] Hari Om & Subhasish Banerjee. A password authentication method for remote users based on smart card and biometrics, Journal of Discrete Mathematical Sciences and Cryptography, 20:3, 595-610 (2017).
[4] Sapna Sharma & Shilpy Agrawal. Personal authentication based on vascular pattern using finger vein biometric, Journal of Discrete Mathematical Sciences and Cryptography, 24:5, 1167-1178 (2021).
[5] H. D. Supreetha Gowda, G. Hemantha Kumar & Mohammad Imran. Multi-modal biometric system on various levels of fusion using LPQ features, Journal of Information and Optimization Sciences, 39:1, 169-181 (2018).
[6] S. K. Sharma, C. S. Lamba, and V. S. Rathore, “Radius based cellular automata approach for image processing applications,” 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-5 (2017), doi: 10.1109/ICCCNT.2017.8203990.
[7] J Cao, L Chen, M Wang and Y Tian, “Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform”, Hindawi Computational Intelligence and Neuroscience (2018).
[8] Z Dou, M Song, K Gao, and Z Jiang, “Image Smoothing via Truncated Total Variation”, IEEE conference proceeding paper on computer vision (2017).
[9] D Wang, “Extremely Optimized DRLSE Method and Its Application to Image Segmentation”, IEEE open access journal, Vol. 7, pp. 119603-119619 (Sep. 2019).
[10] Z Wang and W Wang, “The research on edge detection algorithm of lane”, Springer EURASIP Journal on Image and Video Processing (2018).
[11] M. Hammad, Y. Liu and K. Wang, “Multimodal Biometric Authentication Systems Using Convolution Neural Network Based on Different Level Fusion of ECG and Fingerprint,” in IEEE Access, Vol. 7, pp. 26527-26542 (2019), doi: 10.1109/ACCESS.2018.2886573.
[12] Baby Sherin C and Mredhula L, “A Novel Method for Edge Detection in Images Based on Particle Swarm Optimization”, IOP publishing, Journal of Physics (2017).
[13] Soni, Y. Improved and secure framework for existing e-Bazaar model in Rajasthan. Journal of Information and Optimization Sciences, 43(8), 1975-1985 (2022). https://doi.org/doi.org/10.1080/02522667.2022.2111042.

Views: 469Downloads: 121Citations: 1