Group Face Recognition System

In recent years, schools and universities have been actively working to improve their organizational structure, particularly their support services. Ordinary techniques such as roll calls and ID card sifting are time-consuming, prone to mistakes, and require basic manual effort. These out-of-date systems often lead to irregularities in records and inefficient processes, especially in gigantic classrooms where tracking individual students can be challenging. To tackle these limitations, this endeavor proposes a facial recognition system that automates and optimizes the attendance process through the application of facial recognition technology. This attendance system works in real time, recognizes student faces, recognizes votes on archived records, and carefully checks participation. This limits the need for manual entries, reduces the burden on individuals, and improves overall accuracy. The databases operated by the system certainly hold existence records, allowing for optimized persecution and access to information. The user interface allows teachers and administrators to efficiently create reports and design support screens. By leveraging the user interface, educators and administrators can generate reports and design support screens effectively. A major advantage of this system is its versatility, enabling its deployment across various educational institutions, from schools to colleges. Through the use of Haar cascade for detecting faces and LBPH for recognizing them, this project offers a prompt, effective, and contactless strategy for managing attendance. The research illustrates the substantial impact that artificial intelligence and automation can have on education. During the evaluation process, the system achieved an attendance marking accuracy of 82%, showcasing a considerable degree of effectiveness in standard scenarios.