Biometric ATM Authentication System

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.

The goals of this study were to develop and evaluate a model for acceptance of biometric-based access control for payment technologies used in commercial Banks. A wide diversity of systems requires reliable personal detection schemes to either authenticate or establish the identity of an entity requesting their services. The rationale of such schemes is to ensure that the rendered services are accessed only by a rightful user and no one else. In this paper, a biometric authentication system was modeled. This scheme eliminates the tension of cards which requires one to memorize theirs difficult passwords and pin numbers. The study centered on establishing how biometric authentication can be used in payment technology to boost e-card security. The target population included customers and DTB staff at Kisii branch. Simple random sampling technique was engaged to select respondents. The necessary data was collected using closed-ended questionnaires for easy of analysis. This data was then tabulated and presented in graphs for interpretation purposes. The researcher used the research study findings to design and develop a model, which was called BAECPT (Biometric- Based Authentication E-Card Payment Technology). The requirements for this model were obtained from the responses that the target population gave. Some research constructs loaded low than the theoretical established threshold values and was therefore dropped, while the ones that loaded higher than these values were retained. These are the ones that were included in the final model. Keywords: BAECPT, E-Cards, Model, SEM. Abbreviations: Diamond Trust Bank (DTB), TAM (Technology Acceptance Model), PIN (Personal Identification Number), ATM (Automatic Teller Machine), Structured Equation Modeling (SEM).

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.

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.

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.

This paper investigates the potential of gait-based authentication for securing virtual environments, specifically within the Metaverse. With the growing need for reliable and secure identity verification in virtual spaces, traditional authentication methods, such as passwords or PINs, have proven insufficient. In contrast, biometric authentication systems, including gait analysis, provide a more secure and user-friendly alternative by leveraging unique physiological and behavioral traits for identity verification. This research applies machine learning algorithms—Random Forest and Support Vector Machine (SVM)—to gait data for distinguishing between authentic users and imposters. The dataset consists of 1,000 simulated gait samples with 16 features, such as stride length, step frequency, joint angles, and ground reaction forces (GRF). After performing exploratory data analysis (EDA), including feature distribution visualization and correlation analysis, two models were trained on the data. The Random Forest model outperformed the SVM model, achieving an accuracy of 56% and a recall of 76%, indicating its effectiveness in identifying authentic users. Despite the promising results, both models showed only marginal improvement over random guessing, highlighting the need for further optimization. This study contributes to the growing body of research on gait-based biometric systems by demonstrating their potential as a viable method for identity verification in virtual environments. It also identifies the most important gait features, such as step frequency, cadence variability, and knee joint angle, that significantly contribute to the classification process. Future research should explore advanced deep learning techniques and the integration of multimodal biometric systems to enhance the performance and reliability of gait-based authentication.

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.

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.

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.

"This paper explores the application of neural networks in multimodal biometric authentication systems, emphasizing the integration of multiple biometric modalities to enhance security, accuracy, and robustness against various attacks. Traditional authentication methods, such as passwords and single-modal biometrics, often suffer from vulnerabilities, including spoofing, environmental factors, and data breaches. To address these challenges, multimodal authentication systems combine several biometric traits, such as facial recognition, fingerprint scanning, voice recognition, and keystroke dynamics, to achieve higher reliability and resistance to security threats. The study provides an overview of public datasets used for training neural network-based biometric authentication models, including VoxCeleb, RAVDESS, MOBIO, and SDUMLA-HMT. These datasets contain diverse biometric information necessary for developing robust multimodal authentication systems. The paper evaluates the effectiveness of existing approaches using key performance metrics such as accuracy, false acceptance rate (FAR), false rejection rate (FRR), and area under the curve (AUC). Additionally, specialized metrics are considered, including failure to enroll rate (FTE), failure to acquire rate (FTA), and template stability (TS), which are crucial for real-world applications. The role of neural networks in multimodal biometric authentication is analyzed by examining state-of-the-art architectures, including convolutional neural networks (CNNs) and deep learning-based feature fusion methods. Various fusion levels—feature-level, score-level, and decision-level—are discussed to determine the optimal integration strategy for improving authentication performance. The results indicate that multimodal systems significantly outperform unimodal authentication methods by reducing vulnerability to spoofing and environmental variations. Experimental findings suggest that integrating multiple biometric traits enhances the system’s adaptability to dynamic conditions, reducing both false acceptance and false rejection rates. Despite these advantages, several challenges remain, including computational complexity, data privacy concerns, and the need for real-time processing capabilities. Future research should focus on optimizing multimodal fusion techniques, improving generalization across different datasets, and enhancing the security of biometric templates against adversarial attacks. Additionally, developing lightweight neural network architectures suitable for mobile and embedded systems is essential for the practical deployment of multimodal authentication technologies"

비밀번호를 입력하는 과정에서 가장 큰 보안 위협은 비밀번호를 훔쳐보는 것이다. 비밀번호 훔쳐보기는 비밀번호를 입력하는 과정을 옆에서 지켜보고 비밀번호를 획득하려고 하는 행위로서 비밀번호를 획득하는 가장 전통적인 방법이며 강력한 보안 위협이다. 본 논문에서는 인지심리학에 기초한 비밀번호 훔쳐보기로부터 안전한 기술인 역동 인증 체계(DAS)라 불리는 비밀번호 입력 기술을 제안하였다. 그리고 제안한 역동 인증 체계의 비밀번호 훔쳐보기에 대한 안전성을 직관적인 관점, 훔쳐보기 실험, 이론적인 분석으로 구분하여 검증하였다. 비밀번호 훔쳐보기로부터 안정성이 입증된 역동 인증 체계를 내장시킨 비밀번호 입력기를 설계하여 구현하였다. 구현한 비밀번호 입력기는 일반 패스워드 입력방식 보다 훔쳐보기 공격자의 비밀번호 획득 확률을 현저하게 낮출 수 있으므로 은행에서 운영하는 금융자동화기기에 적용 및 운영되기에 적합한 것으로 평가를 받아 금융기관에서 도입하여 활용되고 있다. When entering the PIN(personal identification number), the greatest security threat is shoulder surfing attack. Shoulder surfing attack is watching the PIN being entered from over the shoulder to obtain the number, and it is the most common and at the same time the most powerful security threat of stealing the PIN. In this paper, a psychology based PINpad technology referred to as DAS(Dynamic Authentication System) that safeguards from shoulder surfing attack was proposed. Also, safety of the proposed DAS from shoulder surfing attack was tested and verified through intuitive viewpoint, shoulder surfing test, and theoretical analysis. Then, a PINpad with an internal DAS that was certified for its safety from shoulder surfing attack was designed and produced. Because the designed PINpad significantly decreases the chances for shoulder surfing attackers being able to steal the PIN when compared to the ordinary PINpad, it was determined to be suitable for use at ATM(automated teller machine)s operated by banks and therefore has been introduced and is being used by many financial institutions.

This research paper aims to contribute to the field of biometric attendance systems. Biometric system which uses unique biological traits like fingerprints for identification and authentication, offers several benefits over traditional authentication methods like passwords or ID cards. The benefits of using biometric system can be enhanced security, convenience and high efficiency, reduced risk of fraud, cost-effective in long run, accountability, scalability, multi-factor authentication, etc. In most of the organizations or institutes attendance is taken by the traditional methods like register book but this method takes time then attendance from these registers is uploaded to server for further attendance report of students. To withstand this problem, we have proposed an IoT based multipurpose biometric authentication system. We have developed a portable system that includes fingerprint sensors to recognize the fingerprint of various students and show the details of the students whose fingerprints have been scanned. The data from the fingerprint scanner is sent to the Raspberry PI server using Wi-Fi. We also have also designed a user interface for students and faculty to view and manage the students and their attendance. The attendance report can be exported to excel file any time faculty want the report. The biometric system provides 80% efficiency over the regular attendance methods.

In recent years, biometric, or "who you are," authentication has grown rapidly in acceptance and use. Biometric authentication offers users the convenience of not having to carry a password, PIN, smartcard, etc. Instead, users will use their inherent biometric traits for authentication and, as a result, risk their biometric information being stolen. The security of users' biometric information is of critical importance within a biometric authentication scheme as compromised data can reveal sensitive information: race, gender, illness, etc. A cancellable biometric scheme, the "BioCapsule" scheme, proposed by researchers from Indiana University Purdue University Indianapolis, aims to mask users' biometric information and preserve users' privacy. The BioCapsule scheme can be easily embedded into existing biometric authentication systems, and it has been shown to preserve user-privacy, be resistant to several types of attacks, and have minimal effects on biometric authentication system accuracy. In this research we present a facial authentication system which employs several cutting-edge techniques. We tested our proposed system on several face databases, both with and without the BioCapsule scheme being embedded into our system. By comparing our results, we quantify the effects the BioCapsule scheme, and its security benefits, have on the accuracy of our facial authentication system.

A Fingerprint-based Authentication Framework for ATM Machines The security of the transactions on the Automated Teller Machine (ATM) has been raising many concerns in some parts of the world. These concerns are due to series of limitations in the existing designs of the various service points. The current use of Personal Identification Number (PIN) for ATM user’s verification and identification has been susceptible to unauthorized access, misplacement, forgetfulness, card swallowing among others; thereby narrowing down the machine’s acceptability and patronage. In this paper, a framework for fingerprint authenticated ATM application is presented. The framework comprises of modules for fingerprint enrolment, database and verification. The verification module comprises of fingerprint enhancement, feature extraction and matching sub-modules, which all rely on suitable mathematical models to function. There is also platform for financial transaction including withdrawal and balance enquiries. The implementation was carried out using Windows 7 as operational platform while C# and Microsoft SQL server served as the frontend and backend engines respectively. Tests conducted on the evaluation of the application using False Rejection Rate (FRR), False Acceptance Rate (FAR) and Average Matching Time (AMT) show the adequacy and the suitability of the proposed framework for ATM user verification and authentication.

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.