Abstract
Attendance plays a major role in every education system. Taking attendance of students manually can be a great burden for teachers. It may cause many problems like loss of time, repetition, incorrect markings, and difficulties in marking them. To avoid this, there is a need to design an automatic system that overcomes the issues with the traditional attendance system. There are many automatic methods available for this purpose like fingerprint systems, RFID systems, face recognition systems and iris recognition systems, etc. But among these Face Recognition proved to be more efficient. The main objective of this paper is to propose a model that captures images from videos, detect and recognize the faces, predict the recognized face, and then marks attendance. In this work, a basic step has been performed which uses fifteen classes from LFW Dataset and faces are detected, recognized, and then prediction is done on a randomly selected image from the used dataset. This system uses a combination of Multi Task Cascaded Neural Network (MTCNN) algorithm along with FaceNet that can be used to detect faces and extract facial features from images. SVM is used to predict the face of the person from the image. The proposed system obtains a classification accuracy of 99.177% for the training set and 100% on the test set. The accuracy, precision, recall, and F1-score are computed.
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Varsha, M., Chitra Nair, S. (2022). Automatic Attendance Management System Using Face Detection and Face Recognition. In: Nayak, P., Pal, S., Peng, SL. (eds) IoT and Analytics for Sensor Networks. Lecture Notes in Networks and Systems, vol 244. Springer, Singapore. https://doi.org/10.1007/978-981-16-2919-8_9
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DOI: https://doi.org/10.1007/978-981-16-2919-8_9
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