Speech-enabled machine learning-based automated attendance monitoring system through face recognition

Verification, and validation are important issues in computerized systems for security in almost every domain. Amongst the many authentication methods, identification via Face ID is an important task. Face recognition plays a huge role in authenticity and is majorly used in the applications of theft prevention, door control systems (DCS), video surveillance systems, and aerial drones. Most universities still rely on traditional methods such as manual name-calling or paper-based record-keeping, which are both inefficient and prone to errors while taking attendance. To make this attendance monitoring system more hassle-free, in this paper, we have proposed and implemented an automated speech-recognized attendance monitoring system primarily based on image processing, speech recognition, and the machine learning algorithm LBPH. The method proposed in this paper aims to take attendance by the teacher in a full-fledged Speech automated system. The system automatically accumulates the required information in the classroom environment and records their attendance with the teacher's commands by taking live images through a camera. Our proposed system has been tested as robust and efficient in automating the monitoring task.