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Our entire system has proven to gather high accuracy in face detection and performance.įawaz, A. The designed system performs efficient in real time implementation for counting and detection. YOLO V3 will first count the students in an image followed by identifying faces as known and unknown generating spreadsheets separately and an email is send at the end of month to students, parents and faculty. The unique part is camera installed in classroom will take picture twice one at the start and one at the end to ensure students has attended complete class. Our unified structure is based on YOLO V3 (You only look once) algorithm for face detection and Microsoft Azure using face API for face recognition (face database).

Some of popular object detection algorithms are back propagation neural network, region based convolution network (RCNN), faster RCNN, single shot detector. In this paper attendance monitoring will be done through smart phone available with almost all faculty members. In Modern era everyone has Smartphone and connected via internet every time.

Current biometric attendance system is not automatic that’s why wastes a lot of time, difficult to maintain and requires a queue for scanning fingerprints to mark their attendance. Traditionally, the attendance of students has been a major concern for the colleges and the faculty has to spend a lot of time and is a tedious job to mark attendance manually.

Real time attendance