ABSTRACT
A face recognition attendance system is a biometric technology that uses artificial intelligence to identify and verify people based on their facial characteristics. It's a fast, high-accuracy system that's used in various sectors, including finance, retail, government, and industry. Attendance systems have evolved significantly over the years, with the advent of technology providing innovative solutions to streamline the process. Facial recognition has emerged as a promising technology in this domain due to its accuracy, efficiency, and non-intrusiveness. The system operates by capturing facial images using a camera and employing Machine learning algorithms to detect, extract, and analyze facial features. Utilizing Machine Learning Algorithm, we develop Face encoding of the captured images and then compare it with encoding present in database of the stored images. The system's architecture encompasses both hardware and software components, including camera modules, processing units, and a centralized database. The proposed system has been evaluated through extensive testing and validation, demonstrating promising results in terms of accuracy, speed, and reliability. Furthermore, the system's adaptability and scalability make it suitable for various industries, including education, corporate environments, and public institutions.
