Biometric-Based Authentication Model for E-Card Payment Technology

As technology progresses, traditional authentication methods like Personal Identification Numbers (PINs) and passwords are becoming more prone to security breaches. To address this issue, biometric-based authentication systems are being seen as a reliable solution to boost security in different areas, such as Automated Teller Machines (ATMs). This study delves into the deployment, advantages, obstacles, and future potential of a biometric-based ATM system. By examining current research, real-life examples, and technological innovations, the aim of this paper is to shed light on the efficiency and viability of biometric authentication in ATM systems. Key Words: Enhanced Security, Authentication, Efficiency, Reliability

Authentication based on biometric provides various advantages in ATMs (Automated Teller Machine) systems. The weakness of existing authentication scheme in ATM is the usage of PIN (Personal Identification Number) numbers as password. Because PIN numbers are easily traceable and misused. In order to achieve security and to overcome these illegal activities, our proposed system is developed to provide better security to the ATMs. Here, the PIN numbers are replaced with biometric security. The main objective of the work is to eliminate the use of ATM cards completely and to ensure better security. In this proposal, we provide the idea of using Aadhaar number as user ID and fingerprint as password. After biometric verification, the user will be allowed to proceed with the transaction. In case of three successive wrong attempts, the account will be blocked.

The increasing reliance on Automated Teller Machines (ATMs) has highlighted the urgent need for advanced authentication mechanisms to safeguard user transactions against fraud and unauthorized access. However, traditional methods such as Personal Identification Numbers (PINs) and passwords remain vulnerable to attacks, while unimodal biometric systems face challenges of inter-class variance, environmental interference, and non-universality. Specifically, single-modality approaches and conventional ATM cameras fall short in capturing reliable biometric features under varying conditions, while the effectiveness of bimodal approaches in such environments has not been adequately investigated. Therefore, this study developed a bimodal biometric authentication system integrating face and iris recognition with Gray Level Co-Occurrence Matrix (GLCM) for enhanced ATM security. The system leverages GLCM for powerful texture feature extraction from both modalities, capturing intricate spatial relationships that are difficult to spoof. The extracted feature vectors were used to train Support Vector Machines (SVM) with a Radial Basis Function (RBF) kernel as classifiers for both face and iris recognition. The final authentication decision was made using Boolean OR rule fusion. The system achieved a remarkable accuracy of 98.2%, with a False Acceptance Rate (FAR) of 1.8% and a False Rejection Rate (FRR) of 1.2%. These results demonstrably outperformed comparable unimodal systems and existing biometric ATMs, validating the proposed framework as a highly secure and efficient solution for financial authentication systems.

Purpose: The study sought to establish the effects of electronic banking on the performance of Commercial Banks in Kenya. The study specifically sought to establish the effects of mobile banking, Electronic Funds Transfer (EFT), Point of Sale Banking, and Automated Teller Machines (ATM) banking on the performance of Commercial Banks in Kenya. The study was anchored on the Technology Acceptance Model, Diffusion of Innovation Theory, and Perceived Characteristics Theory.
 Methodology: A descriptive research design in the form of a longitudinal was adopted in the study. The study targeted licensed Commercial Banks in Kenya. Secondary data was used in the study. The data was gathered from commercial banks' audited financial reports published by the Central Bank of Kenya between 2015 and 2019. The study employed both inferential and descriptive statistics in analysing the collected data. Both Statistical Package for Social Scientists (SPSS) software and MS Excel were adopted to generate the statistics.
 Findings: The study found a relatively strong relationship between independent variable and dependent variable. The combination of independent variables had a fairly predictive potential for financial performance. A proportional increase in the EFT values would significantly increase the return on equity (ROE). It implies that bank performance is positively affected by an increase in EFT banking at a significant level. Similarly, the findings indicated a significant increase in the total value transacted on PoS Machines as a proportion of Total Transaction Value, would increase the return on equity. This shows that bank profitability would increase with increased PoS banking. On the contrary, a percentage rise in the total value transacted via ATM as a proportion of total transactions by value would decrease the return on equity. Similar results were observed with regard to mobile banking’s influence on bank’s ROE.
 Unique contribution to theory, practice and policy: Thus, the study concluded that banks could reduce poor performance by having fewer amounts transacted via mobile banking agents and ATMs. On the other hand, banks can improve performance by encouraging increased values transacted via EFT and on POS Machines.

Providing security to bank customers is the prime responsibility of banking institutions. These institutions upgrade their services to provide convenience-based banking exposure. Automatic Teller Machines (ATMs) assist the bank's customers to operate independently. ATMs verify the authenticity of the user through a 4-digit personal identification number (PIN) only. The State Bank of India (SBI) has initiated a double security check system to minimize the probabilities of banking frauds through ATMs. SBI customers must provide a one-time password (OTP) along with a PIN to complete the cash withdrawal transaction through the SBI ATM. The objective of the study is to analyze the impact of financial security enhancement in ATMs cash transactions through one-time password on the probabilities of banking frauds through ATMs. The study has used primary data collected through a questionnaire from 422 respondents. The study resulted that one-time password has enhanced the financial security function of ATMs and now the probabilities of ATM frauds has been reduced substantially.

In recent years, Automated Teller Machines (ATMs) are witnessed as the most widely used gadgets which required personal identification numbers (PINs) for authentication along with ATM cards. But PINs are suspected of many threats like a man-in-the-middle attack, spoofing, and eavesdropping, etc., which can affect secure and private information. In this paper, we present a novel secure authentication system for ATMs. In this paper, we replace ATM card with facial expression authentication and makes ATMs intelligent enough to recognize users and give them access to their accounts. Replacement of cards will solve the current problem of skimming data from cards with the use of skimmers.

Nowadays Automated Teller Machines (ATMs) are widely used by people. People are dependent upon ATMs to conveniently meet their day-to-day needs. As it is an important factor there comes security. ATMs are electronic machines that are operated by customers to deposit or withdraw cash from banks. It is observed that the number of crimes related to ATMs is increased hence there is a need to provide better security to ATM machines. There are different technologies that are used to provide security to ATM machine which includes – RFID technology, fingerprint, face recognition, iris scan, OTP, reference number, random keypad, etc. In a traditional ATM system card and PIN numbers are used for authentication, where security plays a big concern, such as losing cards, stolen pin numbers, card cloning, shoulder surfing, fake keyboard, skimming, etc. In order to overcome these issues, this paper discusses various systems where ATM cards and pins are replaced by biometrics and how they made ATMs more secure.

With the increase of automated teller machine (ATM) frauds, new authentication mechanisms are developed to overcome security problems of personal identification numbers (PIN). Those mechanisms are usually judged on speed, security, and memorability in comparison with traditional PIN entry systems. It remains unclear, however, what appropriate values for PIN-based ATM authentication actually are. We conducted a field study and two smaller follow-up studies on real-world ATM use, in order to provide both a better understanding of PIN-based ATM authentication, and on how alternative authentication methods can be compared and evaluated. Our results show that there is a big influence of contextual factors on security and performance in PIN-based ATM use. Such factors include distractions, physical hindrance, trust relationships, and memorability. From these findings, we draw several implications for the design of alternative ATM authentication systems, such as resilience to distraction and social compatibility.

This study investigates the impact of Automatic Teller Machines (ATMs) on customer satisfaction among users of digital services at commercial banks in Afghanistan, specifically Afghanistan International Bank, Azizi Bank, and Islamic Bank of Afghanistan. The banking sector in Afghanistan requires further development in expanding ATM infrastructure and enhancing customer satisfaction through improved user experience and support services. Utilizing a quantitative approach with multiple linear regression analysis, the research examines primary data collected through questionnaires as well as secondary data sources. The findings reveal a significant positive relationship between ATM usage and customer satisfaction. To enhance ATM availability and customer satisfaction, commercial banks in Afghanistan should strategically install and maintain ATMs, focusing on key locations in Kabul. Investing in ATM facilities can improve various dimensions of customer satisfaction.

An automated teller machine (ATM) is an electronic device that allows customers of a financial institution to access their account and to perform financial transactions such as checking their account balance and withdrawing cash without the need for a bank teller. The customer is issued with a plastic card with a magnetic stripe (or a chip) that contains a card number and security information that uniquely identifies the customer. The customer inserts the card into the machine and enters the personal identification number (PIN), which must match the PIN stored in the card. There are several million ATMs in use around the world.

Security is an increasingly important issue for business, and with it, the need for authentication. The use of biometric systems for personal authentication is a response to the rising issue of authentication and security. As with any new technology, user acceptance is often hard to measure, thus, in this work, a study of user acceptance of a biometrics authentication system in e-commerce, such as online banking within the Saudi society, was conducted. The study examined whether Saudis are practically willing to accept this technology by involving 306 participants in a large scale laboratory experiment that actively tested a biometric authentication system in combination of a survey. The Technology Acceptance Model (TAM) was adopted as the theoretical basis to develop the research framework in which the model has proven its efficiency as a good predictor for the study’s application.

Nowadays we are experiencing a radical increase in skimming in the Automated Teller Machine (ATM) systems. So, actuation in advancement and security of the ATM machines is required.An automated teller machine (ATM) is an electronic telecommunications device that helps customers of banking departments in transactions and transfer of money in their accounts. The customer enters their unique personal identification number (PIN), i.e. stored in the chip of the card. Due to an increase in the installation of ATM and the number of ATM cardholders, the number of cases of fraudulence has also increased radically. The advancement in technology has resulted in an increase in various skimming activities.So, developments are incorporated in the existing systems to make it more secure, convenient and reliable. The employed secured system must have high speed and must be durable. The presented design is unique because of biometric scanners such as Iris scanner and the two-way check with fingerprint scanner makes it more reliable. The iris scanner being the primary security check lets the system access the further steps for transaction. Fingerprint scanner embedded in the ATM card acts as the secondary security check for the system. The transaction procedure is successful only if the input data by the card holder matches with the database. It consumes less energy that makes it suitable for use. The suggested modified system is pragmatic moreover economical when correlating to the alternative existing classification and affirmation processes of ATMs.

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A wide variety of security applications like computers, laptops, mobile phones, ATM machines, physical access control to property etc. are using biometrics based system for human identification. Today’s Biometrics authentication system can provide highest level of security to the user of the service and the service providers. Today’s uni-modal biometric systems has become mature system and being used for low level to medium level security applications. But, for extremely higher level of security requirements like military base, nuclear plants, research laboratories etc., Finger knuckle image and Retina image based combination can be used, as this combinations provides the highest level of accuracy, low error rates, universality etc. Also the security of Multi-modal biometric templates is very importance as biometric template store vital information of the user. In this research work, Multi-modal Biometric system for higher level security using Finger Knuckle image and Retina image with templates security is developed, experiments are carried out and results are calculated using PolyU database for Finger Knuckle image and DRIVE for Retina images. A Finger Knuckle Image processing starts with Median Filtering, CLAHE method and Canny edge detection. The features extraction is carried using SURF feature method. Once the finger knuckle image template is generated, it is encrypted using RSA algorithm and stored in database. The Retina Image Processing is carried out by applying Green channel followed by Gaussian filtering, CLAHE method is applied on outcome of Gaussian filtering. SURF features set is extracted from retina ROI. Retina image template is generated which is later encrypted using RSA algorithm. The final experiment shows that genuine acceptance rate = 100% is achieved for threshold value 0.35 and False Acceptance Rate is only 1%. The scientific contribution of proposed research work is a multi-biometric system with higher accuracy (GAR is 100%), low error rate (FAR is 1%), better template protection by RSA encryption and efficient hybrid model using finger knuckle image and retina image.

Traditional identity verification of students based on the human proctoring approach can cause a scam identity verification and ineffective processing time, particularly among vast groups of students. Most student identification cards outdated personal information. Several biometric recognition approaches have been proposed to strengthen students’ identity verification. Most educational adoption technologies struggle with evaluation and validation techniques to ensure that biometric recognition systems are unsuitable for utilization and implementation for student identity verification. This study presents the internet of things to develop flexible biometric recognition systems and an approach to assess the quality of biometric systems for educational use by investigating the effectiveness of identity verification of various biometric recognition technologies compared to the traditional verification method. The unimodal, multimodal, and semi-multimodal biometric technologies were tested using the developed internet of things-base biometric recognition systems examined by applying the proposed quality metrics of scoring factors based on accuracy, error rate, processing time, and cost. Hundreds of undergraduate exam takers were a sample group. Key findings indicate that the designed and presented systems suitably attain identity verification of exam students using a unimodal biometric. The unimodal facial biometric system promises excellent support. A unimodal fingerprint biometric system ensures second excellent aid for student identity verification. However, multimodal and semi-multimodal biometric systems provide better accuracy with fewer handling times and higher costs. This study contributes significantly to the knowledge of utilizing biometric recognition for identity verification in smart educational applications.