Security Enhancement through IRIS and Biometric Recognition in ATM

In today's world technology has advanced to such an extent that it is interchangeable with connection and convenience. ATM was one of the major breakthroughs, and over the time it has provided better convenience in fulfilling one’s banking needs. Although, there are certain predicaments that such ATM transactions are susceptible too. The conventional PIN based authentication that is presently accustomed in all ATM apparatus is liable to shoulder surfing, hassle in remembering the multiple PIN and the rest. The physical card brings along setbacks in particular, wearing out of the magnetic strip attributable to frequent usage, losing or getting it stolen. Aside from these there are other unlawful activities that are carried upon. The objective of this paper is to present a solution to the above stated problems. In contrast to standard architecture, the proposed solution incorporates NFC enabled smartphones as a substitute for physical card and iris based authentication for PIN.

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“…Tyagi et al [25] here a bimodal biometric system has been brought forward. First the user enters the amount to be withdrawn.…”

Section: Table 32 Different Communication and Authentication Technologies Availed By The Researchers In Their Workmentioning

confidence: 99%

Enhanced Secure ATM authentication using NFC Technology and Iris Verification

Agrawal

Oza

Biswas

et al. 2021

SCPE

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“…Recognition in a person has a unique characteristic on a person's body, which cannot be imitated. Recognition methods are much more difficult to hack, reconstruct, and fake [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Voice Recognition Security Reliability Analysis Using Deep Learning Convolutional Neural Network Algorithm

Ibrahim

Candra

Isyanto

2022

JET

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This study discusses the reliability analysis of voice recognition security using the deep learning convolutional neural network (CNN) algorithm. The CNN algorithm has learning advantages in that it is safer, faster, and more accurate. CNN also can solve user identification problems in large amounts of data. The measured voice input is ten types of user's voice with the number of iterations of 6000, 12000, and 15000 sound files. Furthermore, voice extraction features are performed to recognize conversations and retain information that is very much needed. After that, the voice file iteration data is trained to register the user's voice so that a trained model is obtained. These results measure performance (confusion matrix) to analyze the actual value compared to the predicted value in the CNN algorithm. The results obtained are that the best accuracy is obtained at 15000 sound file iterations, 96.87%, 12000 sound file iterations get 96.30%, and 6000 sound file iterations get 95.77%. CNN's performance data shows that 15000 iterations of voice files produce high accuracy. Voice recognition security helps provide high security and maintain the privacy of one's identity.

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