No abstract

—Automated Teller Machine (ATM) frauds have increased over the years resulting in the loss of customers and trust in current ATM security measures. In order to fight identity fraud and regain trust and reliability, biometric recognition technologies have become a necessity for ATMs. Using a card and pin is no longer reliable due to the rise of ATM identity fraud over the years. Studies have been carried out to solve this uprising issue, such as replacing the memorized pin with a one-time pin (OTP) and others using biometrics. There are still many gaps and challenges in the systems, with a common challenge being their accuracy rate, dependencies, and reliability. This study is conducted to alleviate the problem by introducing a versatile multimodal biometrics system. After a careful review of different biometric techniques and their differences in human identification inboth accuracy and reliability, physical biometrics isadopted in this study. Unlike behaviouralbiometrics, physical biometric features cannot easily be copied, replicated, or mimicked, and they are unique per individual. The three body features chosen for this study are the face, fingerprint, and iris. A real-time feature quality evaluation method is employed to assess the reliability of the biometric recognition results. The system provides independent anytime ATM access and prevents card-based theft and fraud, it can be relied on to always deliver uninterrupted ATM services to customers. Customers register their fingerprint, face, and iris to be able to use the biometric ATMs. A combination of any two of the registered biometric features can be used to authenticate users with high accuracy and reliability on ATMs with a 0% false acceptance rate. It has been found that allowing multiple options for users reduces false rejections and provides a 100% ATM access guarantee.