A novel and smarter model to authenticate and identify people intelligently for security purposes

In recent years, security concerns, including document fraud and identity theft, terrorism, and cybercrime have led to the growth and development of biometric technologies, and in general, all activities related to biometric research have gained momentum. This research presents a more intelligent methodology for authenticating and verifying one's identity while reading and typing a text. Threats to e-commerce security are wreaking havoc on online trade via a variety of nefarious methods. Thus, this method can be implemented to furtively identify these attacks and impede them, using a variety of texts to study. We refer to the authentication as a behavioral biometric technology based on how people look when typing in which the visual information was implemented to verify the identity of 35 users while typing via using an eye tracker device. Hence, we have employed the two-layer perceptron neural network to model the learning phase in the object recognition process. The fusion of eye tracker device information and two-layer perceptron neural network coefficients was done at the decision level, and we have achieved an average of 98.97%, 98.76% and 98.86% for sensitivity, specificity and accuracy, respectively. By the combination of biometric technology products as well as modern computer technology, it is easy to perform monitoring, management, systems integration, automated management, and security applications.