Paper Title : Multiparty NetBanking Security Based on Face Biometrics Using Deep Learning Algorithm

ISSN : 2394-2231
Year of Publication : 2021

10.29126/23942231/IJCT-v8i2p43

Authors: A.Sumathi, S.Kalpana, A.Soundharya, R.Ranjitha

         



MLA Style: A.Sumathi, S.Kalpana, A.Soundharya, R.Ranjitha " Multiparty NetBanking Security Based on Face Biometrics Using Deep Learning Algorithm " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: A.Sumathi, S.Kalpana, A.Soundharya, R.Ranjitha " Multiparty NetBanking Security Based on Face Biometrics Using Deep Learning Algorithm " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
Internet banking transaction should have layered protection against security threats, the providers should approach security considerations as part of their service offerings. And heard a lot about hackers and crackers ways to steal any logical password or pin code number character, crimes of ID cards or credit cards fraud or security breaches. In existing framework, identification can be equated to a username and is used to authorize access to a system. As usernames can be lost or stolen, it is necessary to validate that the intended user is really the person he or she claims to be – the authentication process. Biometric based authentication and identification systems are the new solutions to address the issues of security and privacy. The Face Recognition is the study of physical or behavioral characteristics of human being used for the identification of person. These physical characteristics of a person include the various features like fingerprints, face, hand geometry, voice, and iris biometric device. So implement real time authentication system using face biometrics for authorized the person for online banking system. The general objective of our project is to develop fully functional face recognition, verification system provide and understand the key aspects of these major technologies, namely those relating to the technological, application entity domain, social environmental system and performance aspects. And also provide multiparty access system to allow the multiple persons to access the same accounts by providing access privileges to original account holders. Experimental results show that the proposed system provide high level security in online transaction system than the existing traditional cryptography approach.

Reference
[1] CAMPUS, KATTANKULATHUR. "CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING MODELS AND COLLATING MACHINE LEARNING MODELS." INTERNATIONAL JOURNAL OF PURE AND APPLIED MATHEMATICS 118.20 (2018): 825-838. [2] RANDHAWA, KULDEEP, ET AL. "CREDIT CARD FRAUD DETECTION USING ADABOOST AND MAJORITY VOTING." IEEE ACCESS 6 (2018): 14277-14284H. [3] SHUKUR, HAMZAH ALI, AND SEFERKURNAZ. "CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING METHODOLOGY." INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MOBILE COMPUTING 8.3 (2019): 257-260. [4] YEE, ONGSHU,."CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING AS DATA MINING TECHNIQUE." JOURNAL OF TELECOMMUNICATION, ELECTRONIC AND COMPUTER ENGINEERING (JTEC) 10.1-4 (2018): 23-27. [5] THENNAKOON, ANURUDDHA, ET AL. "REAL-TIME CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING." 2019 9TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING (CONFLUENCE).IEEE, 2019. [6] AWOYEMI, JOHN O. "CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING TECHNIQUES: A COMPARATIVE ANALYSIS." 2017 INTERNATIONAL CONFERENCE ON COMPUTING NETWORKING AND INFORMATICS (ICCNI).IEEE, 2017. [7] M. YANG, L. ZHANG, “SPARSE REPRESENTATION BASED FISHER DISCRIMINATION DICTIONARY LEARNING FOR IMAGE CLASSIFICATION,” INTERNATIONAL JOURNAL OF COMPUTER VISION, VOL. 109, NO. 3, PP. 209– 232, 2014. [8] M. PANG, B. WANG, , “DISCRIMINANT MANIFOLD LEARNING VIA SPARSE CODING FOR ROBUST FEATURE EXTRACTION,” IEEE ACCESS, VOL. 5, PP. 13978–13991, 2017. [9] P. ZHOU, C. ZHANG, AND Z. LIN, “BILEVEL MODEL-BASED DISCRIMINATIVE DICTIONARY LEARNING FOR RECOGNITION,” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 26, NO. 3, PP. 1173–1187, 2017. [10] T. PEI, L. ZHANG, B. WANG, F. LI, AND Z. ZHANG, “DECISION PYRAMID CLASSIFIER FOR FACE RECOGNITION UNDER COMPLEX VARIATIONS USING SINGLE SAMPLE PER PERSON,” PATTERN RECOGNITION, VOL. 64, 2017

Keywords
———authentication, face recognition, biometric, multiparty access.