Evolving Payment Security: A Facial Recognition-Based Credit Card Reader with A Multifunctional Cascade Neural Network

As digital transaction become increasingly pervasive, secure payment in payment systems is rigours, critical. This paper introduces a novel approach to payment security using facial recognition technology integrated with a credit card system. Our system, a Credit Card Reader Using Face Recognition (CCR-FR), leverages state of the art facial recognition algorithm, paired with encryption techniques, to build a safe and user-friendly payment environment. The methodology involves the capturing and processing facial images via OpenCV and an embedded FaceNet model. These facial images are then encrypted and associated with credit card details. The user registration process ensures that the user is uniquely identified, the credit card information is encrypted, and the data is securely stored in a MySQL database. The results demonstrate the efficiency of the CCR-FR process in both registering and capturing facial features, therefore proving that this type of register procedure results in a seamless user experience, as well as enhancing payment security, demonstrated by its face recognition accuracy and credit card association success rates. This work shows the way to the full integration of face recognition payments systems and is proof that they offer an enhanced level of security with a minimum impact on transaction speeds. The research not only covers the technical aspects but also goes in depth of security aspects including encryption techniques and key management. After providing a glance of the coming future work to enhance our techniques and discuss its current limitations. Our work represents an advancement to payment security which is becoming mature, new payment methods guarantees to be robust to hardware that will see widespread deployment and privacy problems that ensures value to customers.