This research work is proposed to developing a less turmoil, cost effective and robust exercise of autonomous attendance administration system. To watch carefully students’ daily ghost, marking attendance is a ordinary practice for all educational organizations at all levels. There was once a opportunity when attendance was tracked manually. Although they are behind and labor-intensive for a large number of pupils, these approaches are exact and forbid the possibility of deceptive enrollment. To address the disadvantages of manual systems, autonomous wholes based on high frequency recognition flipping through, fingerprint scanning, face acknowledgment, and iris scanning are being grown. Each technique has benefits and disadvantages. However, the majority of these systems are restricted by the necessity for individual human engagement all along the recording of attendance. To close the gaps in the current human and independent attendance management orders, we developed a healthy and efficient attendance recording structure using a sole group shot to detect face labeling and recognition algorithms. A extreme-definition camcorder mounted in a stable position records a series of countenances for each pupil seated in a classroom. The faces are before extracted from the group ammunition using a protocol, and they are identified using a convolutional interconnected system that is familiar to students from a face table. We evaluated our technique utilizing several datasets and group pictures. Our research shows that the submitted framework performs better in agreements of effectiveness, utility, and implementation than the current attendance pursuing methods. The suggested answer is a self-contained attendance arrangement that necessitates little contact betwixt humans and machines, making integration into a smart homeroom simple.

Author(s) Details:

S. Purushothaman,
Department of Electronics and Communication Engineering, V.S.B Engineering College, Karur, Tamil Nadu, India.

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Keywords: Face recognition, attendance recording, smart class room, convolution neural network, autonomous system, iris scanning

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