Development of Facial Detection System for Security Purpose Using Machine Learning

Abstract

Face recognition is a technique for recognizing or authenticating someone’s identification based on a quick glance at their face. After that, this application can employ computer vision to discover a potential face inside its stream. Facial recognition is been used in a various routine operation, from mobile phone unlocking to ATMs. Individuals and businesses use automated teller machines (ATMs) to conduct a spectrum of financial activities, includes banking, for both individuals and organizations. There seem to be ATMs everywhere, such as in restaurants, supermarkets, convenience stores, malls, schools, gas stations, hotels, workplaces, banking facilities, airports, entertainment venues, transportation facilities, and numerous other locations. Consumers often have access to ATMs on a continuous basis, allowing them to conduct financial transactions at any time of day or week. In this project, face recognition and a tiered security mechanism are used. Machine learning, OpenCV, and Python are used to implement face recognition. In this situation, Face embeddings are used to extract characteristics from the face. A neural network uses a picture of a person’s face as input and generates vectors representing the most important face attributes. This vector is called an We refer to it as face embedding since it occurs in machine learning. The project aims to reduce the risks associated with remote ATMs and the problems associated with fraudulent transactions, such as misusing someone else’s card to withdraw money. Therefore, to overcome this problem, we developed a solution using ML to limit card use to just authorized individuals who can be recognized using face recognition software.