International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

EB: Eye Biometrics Based a Novel Human Recognition System for Cardless Online Payment Security Improvement in ATMs

Author : Shubham Verma ¹ Dipti Ranjan ²

Date of Publication :10th September 2022

Abstract: Here we are introducing a novel approach for enhancing the security of traditional transactions of ATM .Individual confirmation is quite possibly the main ways to deal with work on the security. Be that as it may, the conventional individual validation techniques or advance hybrid. Biometrics, which naturally utilizes the physiological or social quality of individuals to perceive their characters, is one of the powerful procedures to defeat these issues. Biometrics is a field of programmed individual ID dependent on physiological and social attributes of people. A conduct trademark is progressively an impression of an individualâ€™s physiological cosmetics Validations. We used Eye Biometric Recognition System using OpenCV for cardless transaction & Identity Verification after ATM Transactions. We proposed EB verification model in the research for final compilation of transaction. Though it may require more time for verification but security is prior to time & cyber thefts. The efficiency of Software we Proposed came out to be 98.52%, 95.75%, 98.86% while performing an extensive testing of our algorithm with 3 datasets named UBIRIS.V1, UBIRIS.V2, IITD and the algorithms we have used in OpenCV For feature detection & extraction are ORB, brute force algorithms. With the hope of Interest in youth and additional feature of security to be embedded in online transaction is the motivation of the research.

Reference :

1. C.L. Tsai, C.J. Chen, D.J. Zhuang, Trusted Mbanking Verification Scheme based on a combination of OTP and Biometrics. Journal of Convergence, 3(2012) 23-30
2. M.K. Sharma, J. Manisha, J. Nene, Dual factor thirdâ€party biometricâ€based authentication scheme using quantum one time passwords, Security and Privacy, 3 (2020) e129.Wiley Online Library
3. Z. Huang, Y. Liu, C. Li, M. Yang, Chen A robust face and ear based multimodal biometric system using sparse representation, Pattern Recognition, 46 (2013) 2156–2168.
4. N. Alay, H.H. Al-Baity, A multimodal biometric system for personal verification based on different level fusion of iris and face traits. Biosci. Biotechnol. Res. Commun, 2 (2019), 565- 576.
5. K. Gunasekaran, J. Raja, R. Pitchai, Deep multimodal biometric recognition using contourlet derivative weighted rank fusion with human face, fingerprint and iris images. Automatika: Äasopis za automatiku, mjerenje, elektroniku, raÄunarstvo i komunikacije, 60(2019) 253-265.
6. Z.T. Liu, C-S Jiang, S-H Li, M. Wu, W-H Cao, M. Hao, Eye state detection based on Weight Binarization Convolution Neural Network and Transfer Learning, Applied Soft Computing,Volume, 109(2021),107565,ISSN 1568-4946,
7. H. Kim, J. Jo, K.A. Toh, J. Kim, Eye detection in a facial image under pose variation based on multi-scale iris shape feature, Image and Vision Computing, 57(2017) 147-164
8. P. Majaranta, 2018. Bulling an Eye tracking and eye-based human–computer interaction, In Advances in Physiological Computing. Springer, London UK, 39–65. [9] L. Åšwirski, A. Bulling, N. Dodgson, 2012. Robust real-time pupil tracking in highly off-axis images. In Proceedings of the Symposium on Eye Tracking Research and Applications, Santa Barbara CA, USA. 173–176.
9. A. PÄƒsÄƒricÄƒ, R.G. Bozomitu, D. TÄƒrniceriu, G. Andruseac, H. Costin, Rotariu CAnalysis of Eye Image Segmentation Used in Eye Tracking Applications. Rev. Roum. Sci. Tech. 62(2017) 215–222.
10. N. Hezil, A. Boukrouche, Multimodal biometric recognition using human ear and palm print. IET Biom, 6(2017) 351–359.
11. A.A. Jarjes, K. Wang, G.J. Mohammed, Improved greedy snake model for detecting accurate pupil contour. IEEE 3rd international conference on advanced computer control, 2011, 515-519.
12. J.D. Bustard, M.S. Nixon, Robust 2D ear registration and recognition based on SIFT point matching, In2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems, 2008, 1-6.
13. J. Zhou, S. Cadavid, M. Abdel-Mottaleb,Exploiting Color SIFT Features for 2D ear recognition, 18th IEEE International Conference on image processing, 2011, 553 – 556.
14. R.A. Priyadharshini, S. Arivazhagan, M. Arun, Deep learning approach for person identification using ear biometrics, Appl Intell 51, 2161–2172 (2021).
15. F. Khursheed, A.H. Mir, AR model based human identification using ear biometrics, International Journal of Signal Processing. Image Processing and Pattern Recognition, 7(2014) 347-360.
16. L. Alvarez, E. González, L. Mazorra, Fitting ear contour using an ovoid model, In Proceedings 39th Annual 2005 International Carnahan Conference on Security Technology, 2005, 145-148. doi: 10.1109/CCST.2005.1594829.
17. B. Zavar, Arbab, S. Mark, On guided model-based analysis for ear biometrics, Comuter Vision and Image Understanding, 115(2011) 487-502.
18. J.G Daugman, High confidence visual recognition of persons by a test of statistical independence, IEEE transactions on pattern analysis and machine intelligence, 15(1993) 1148- 1161. doi: 10.1109/34.244676 [20] G.T. Flitton, T.P. Breckon N.M. Bouallagu, Object Recognition using 3D SIFT in Complex CT Volumes, In BMVC, 1(2010) 1-12. Doi. 10.5244/C.24.11
19. S.S. Sumit, D.R.A. Rambli, S. Mirjalili, Vision-Based Human Detection Techniques: A Descriptive Review, IEEE Access, 9(2021) 42724-42761. Doi: 10.1109/ACCESS.2021.3063028.
20. A. Mordvintsev, K. Abid, OpenCV-python tutorials documentation. Obtenido de https://media, readthedocs. Org/pdf/OpenCV-python-tutroals/latest/OpenCV-pythontutroals. Pdf, 2014.
21. A.S.Al-Waisy, R. Qahwaji, S. Ipson, S. Al-Fahdawi, T.A. Nagem, A multi-biometric iris recognition system based on a deep learning approach, Pattern Analysis and Applications, 21(2018) 783-802. https://doi.org/10.1007/s10044-017-0656-1
22. Z. Wang, C. Li, H. Shao, J. Sun, Eye recognition with mixed convolutional and residual network (MiCoRe-Net), IEEE Access, 6(2018)17905-17912. doi: 10.1109/ACCESS.2018.2812208.
23. B.K. Tripathi, 2014. High dimensional neurocomputing: growth, appraisal and applications. Springer, London.
24. N. Kihal, S. Chitroub, A. Polette, I. Brunette, J. Meunier, Efficient multimodal ocular biometric system for person authentication based on iris texture and corneal shape, IET Biometrics, 6(2017) 379-386. 10.1049/iet-bmt.2016.0067.
25. Z. Zhu, Y. Cheng, Application of attitude tracking algorithm for face recognition based on OpenCV in the intelligent door lock, Computer Communications, 154(2020) 390-397.
26. S. Ren, K. He, R. Girshick , J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks, In: International conference on neural information processing systems (NIPS),2015,91–99.
27. R. Tobji, W. Di, N. Ayoub, FMnet: Iris Segmentation and Recognition by Using Fully and Multi-Scale CNN for Biometric Security, Applied Sciences, 9 (2019) 2042.
28. G. Liu, W. Zhou, L. Tian, W. Liu, Y. Liu, H. Xu, An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network, Sensors, 21(2021) 3721.
29. Y. Chen, C. Wu, Y.Wang, T-center: a novel feature extraction approach towards large-scale iris recognition. IEEE Access, 8 (2020)32365-75. doi: 10.1109/ACCESS.2020.2973433.