Face Recognition and Authentication using DLIB for Examination Attendance System

Facial recognition technology enables the identification and detection of individuals based on unique facial features. based on various labels such as name, ID number, etc. In this research, a face recognition system for examination attendance is implemented using the DLIB library. Since manual attendance is a tedious and time-consuming task, this research aims to automate the process, thereby increasing the integrity of examinations. The proposed solution reduces the workload of invigilators by eliminating the need to manually take attendance and fill out attendance sheets. The dataset for this research comprises a combination of student images captured by a camera and images from the database. The system utilizes the DLIB library to generate facial encodings, which are then used to identify students in real-time. Upon recognizing a student, the system logs their attendance by recording their name and the current timestamp in a CSV file. DLIB has pre-trained CNN models that can be used forface detection. This ensures accurate and efficient attendance tracking, providing a reliable record of student presence during examinations.