International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
A Real Time Face Detection & Security System for ATM Machine. Siddhika Pokharkar1, Bhagyashri Bhalerao2, Pratiksha Dolas3, Dr.Anand Khatri4 123Undergraduate Student, Department Of Computer Science And Engineering, Jaihind College of Engineering,
Kuran, Pune
4Professor, Department Of Computer Science And Engineering, Jaihind College of Engineering,Kuran ,Pune
----------------------------------------------------------------------***--------------------------------------------------------------------Network (CNN) model. To enhance reliability in lowerAbstract - With the increasing incidence of ATM fraud, resource scenarios, a Local Binary Patterns Histogram (LBPH) method is also integrated as a backup. Additionally, a secure PIN verification system is incorporated using cryptographic hashing to ensure data protection.
there is a pressing need for enhanced security measures in automated teller machines (ATMs). This paper presents a dual-factor authentication system that integrates face recognition and Personal Identification Number (PIN) verification to improve ATM transaction security. Utilizing a Convolutional Neural Network (CNN) for real-time face recognition, the proposed system significantly reduces the risk of unauthorized access. The implementation includes an Arduino-based cash dispenser and a web application developed using Tkinter, demonstrating a robust prototype that effectively mitigates ATM fraud..
PROBLEM STATEMENTAutomated Teller Machines (ATMs) are a primary channel for cash withdrawals and banking transactions. However, they remain vulnerable to identity theft, card skimming, and unauthorized access. Traditional ATM systems rely solely on card-based authentication paired with a PIN, which can be stolen, guessed, or phished.
Key Words: Face Recognition, CNN, Dual-factor Authentication, PIN Verification, ATM Security, Tk inter, Arduino, ATM Prototype.
This project aims to design and implement a real-time, dual-factor authentication system that enhances ATM security by integrating facial recognition technology with traditional PIN verification.
INTRODUCTIONThe rise in ATM fraud has necessitated the development of more secure authentication mechanisms. Traditional PIN based systems are vulnerable to various attacks, including shoulder surfing and card skimming. This paper aims to enhance ATM security by integrating biometric face recognition with conventional PIN verification, thereby providing a dual-layered authentication approach. He rise of ATM fraud, there is an increasing need for secure and reliable authentication mechanisms. Traditional PIN-based authentication systems are susceptible to various types of attacks, including shoulder surfing and card skimming. This project aims to strengthen ATM authentication by integrating face recognition along with traditional PIN verification.
The core objective is to ensure that only the rightful owner can access their bank account by validating both physical presence (face) and knowledge (PIN), significantly reducing the chances of unauthorized access and fraud.
LITERATURE SURVEYPrevious research on ATM security has focused on various biometric systems such as fingerprint and voice recognition. These systems, though secure, often require user contact or specialized hardware. Recent developments in facial recognition, especially with convolutional neural networks (CNNs), have demonstrated robust accuracy in non intrusive user authentication. Our approach combines this biometric technique with a traditional PIN interface and includes real-time hardware control, offering a complete security system not commonly found in existing literature.
As the demand for secure and user-friendly banking systems grows, traditional ATM authentication methods based solely on Personal Identification Numbers (PINs) are becoming increasingly vulnerable to theft, skimming, and unauthorized access. To address these security concerns, this project introduces a Real-Time Face Detection and PIN Authentication System for ATM machines that implements dual-factor authentication, combining biometric verification with a traditional PIN entry.
As ATM fraud and identity theft continue to pose serious security concerns, the traditional reliance on Personal Identification Numbers (PINs) alone has proven insufficient. In response, the integration of biometric technologies—particularly real-time face detection—has emerged as a promising solution to enhance ATM security. Combining facial recognition with PIN entry creates a dual-factor authentication system, which significantly
The proposed system leverages computer vision and machine learning techniques to recognize and verify the user's face in real time using a Convolutional Neural
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