Paper Title : Biometric Authentication System

ISSN : 2394-2231
Year of Publication : 2020

10.29126/23942231/IJCT-v7i6p9

Authors:Amit D Mishra

         



MLA Style: Amit D Mishra "Biometric Authentication System " Volume 7 - Issue 6 November - December,2020 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: Amit D Mishra "Biometric Authentication System " Volume 7 - Issue 6 November - December,2020 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
Smart devices are gaining popularity and becoming a key platform for accessing business and personal information in this industry.to access this sensitive data requires a good quantity of authentication and identification. The focus of this paper will be to discuss the limitations of a single biometric system and suggest the need to overcome the limitations to enhance the system performance. Biometrics provides better security solutions than conventional authentication systems because it uses certain physiological or behavioral traits associated with the person.

Reference
1) Wu, W., Elliott, S. J., Lin, S., Sun, S., & Tang, Y. (2020). Review of palm vein recognition. IET Biometrics, 9(1), 1–10. https://doi.org/10.1049/iet-bmt.2019.0034 2) Syazana-Itqan, K., Syafeeza, A. R., Saad, N. M., Hamid, N. A., & Bin Mohd Saad, W. H. (2016). A Review of Finger-Vein Biometrics Identification Approaches. Indian Journal of Science and Technology, 9(32), 1–8. https://doi.org/10.17485/ijst/2016/v9i32/99276 3) Yu, X., & Wang, H. (2013). Recognition and Matching of ROI Region Finger-Vein Based on NMI Feature. Advanced Materials Research, 760–762, 1447–1451. https://doi.org/10.4028/www.scientific.net/amr.760-762.1447 4) Singh, B. (2016). Iris Recognition Using Curve let Transformation Based on Gabor Filter& SVM. International Journal Of Engineering And Computer Science, 1–5. https://doi.org/10.18535/ijecs/v5i8.32 5) Ma, L., Tan, T., Wang, Y., & Zhang, D. (2004). Efficient Iris Recognition by Characterizing Key Local Variations. IEEE Transactions on Image Processing, 13(6), 739–750. https://doi.org/10.1109/tip.2004.827237 6) Iris recognition. (2015). Biometric Technology Today, 2015(8), 12. https://doi.org/10.1016/s0969-4765(15)30142-9 7) Senaratne, R., Halgamuge, S., & Hsu, A. (2009). Face Recognition by Extending Elastic Bunch Graph Matching with Particle Swarm Optimization. Journal of Multimedia, 4(4), 775–779. https://doi.org/10.4304/jmm.4.4.204-214 8) GU, R. U. I. (2018). Overview of Occlusion Face Recognition Technology. DEStech Transactions on Computer Science and Engineering, ceic, 1–8. https://doi.org/10.12783/dtcse/ceic2018/24539 9) Liu, T., Mi, J.-X., Liu, Y., & Li, C. (2016). Robust face recognition via sparse boosting representation.Neurocomputing,214,944–957. https://doi.org/10.1016/j.neucom.2016.06.071 10) Face recognition. (2008). Biometric Technology Today, 16(10), 9–11. https://doi.org/10.1016/s0969-4765(08)70210-8 11) Darwaish, S. F., Moradian, E., Rahmani, T., & Knauer, M. (2014). Biometric Identification on Android Smartphones. Procedia Computer Science, 35, 832–841. https://doi.org/10.1016/j.procs.2014.08.250 12) Ma, L., Tan, T., Wang, Y., & Zhang, D. (2004). Efficient Iris Recognition by Characterizing Key Local Variations. IEEE Transactions on Image Processing, 13(6), 739–750. https://doi.org/10.1109/tip.2004.827237

Keywords
Biometrics, Authentication, Pattern recognition, Humans, Ear Artificial intelligence