Automated Multi-Student Attendance Using Video-Based Facial Recognition and Embedding Matching

Abstract: A major aspect of determining student attendance is having accurate records; unfortunately, traditional attendance systems are inefficient, not accurate, and can be manipulated for ”proxy” attendance. A way around these obstacles is to create an intelligent, automated attendance method using deep learning facial recognition technology based on a pretrained model. In addition to traditional still-image approaches, our system begins with recording a short video of each participating student, leading to frame-by-frame extraction for constructing a robust and diverse dataset containing facial image samples of each student. This new pipeline from video to image will improve the representation of students’ faces by using the three ways students’ faces are captured (pose, expression, and lighting) to provide a more realistic photo of each student. In this approach, we will utilise a pretrained deep learning face recognition algorithm to extract the distinctive features of student faces from our generated dataset. A single classroom image containing multiple students will be used for attendance. The system will identify faces in the group image and extract their unique features by using three face detection methods, namely Haar Cascade, MTCNN, and InsightFace, which are evaluated and compared. For recognition, FaceNet and ArcFace embedding models are investigated using cosine similarity matching to perform both extraction and recognition, which helps us generate face embeddings for each person, compare those features to the database using cosine similarity, and mark attendance for recognized students with no manual intervention. This approach should successfully connect individual-level training data with group-level testing conditions, allowing for accurate facial recognition in the classroom.

Keywords: Automated Attendance System, Facial Recognition, Pretrained Deep Learning Model, Video Frame Extraction.