This paper discusses the development of the face recognition attendance management system, FRAMS. In the development of FRAMS, there are two important stages which are face detection algorithm development and face recognition algorithm development. In face detection algorithm development, three proposed face detection methods which are Viola-Jones Haar Cascade Classifier, Local Binary Pattern, LBP and Multi-Task Cascaded Convolutional Neural Networks, MTCNN are used to evaluate their performance in terms of face detection accuracy and total detection time required by those methods. From those experiments, it can be known that MTCNN is the best method as it provides a 100% face detection accuracy compared to another two proposed methods although the processing time is longer than another two methods. In face recognition algorithm development, the pre-trained VGG-16 CNN model is used to perform transfer learning by loading the train and validation image datasets. The confusion matrix is plotted out to evaluate the performance of the trained CNN model. The trained VGG-16 CNN model achieved an accuracy of 99%. Finally, a complete FRAMS has been developed and able to recognise the face feature of students with extremely few misclassification mistakes.
Face Recognition Attendance Management System (FRAMS) Algorithm Using CNN Model
Lect. Notes Electrical Eng.
International Conference on Robotics, Vision, Signal Processing and Power Applications ; 2021 April 05, 2021 - April 06, 2021
Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications ; Chapter : 49 ; 391-397
2024-03-31
7 pages
Article/Chapter (Book)
Electronic Resource
English
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