Class Monitoring System Tools MTCNN and Haarcascade Classifier

Authors

  • Aditya Vikram Bhattacharya
  • Mrinalini Khanna
  • Akshay Tripathi
  • S Murugaveni

DOI:

https://doi.org/10.14419/ijet.v7i3.12.17609

Published:

2018-07-20

Keywords:

.

Abstract

The project aims towards the assistance of teachers at the time of taking attendance. The system solely focuses on face detection and recognition. The tools used to device the system are API’s offered by Python 3.6, Open CV(for detection) and a few cognitive tools provided by Azure.The basic idea behind the project is face recognition linked to a database backend. The information of the student attending the class is stored here. The entire attendance is associated with two types of time stamps incorporated at the server end. The time stamp helps to keep a track of the hour conducted and the time for which number of people attended the class. Exceptions in the time stamp would be incorporated in order to cater for the students leaving the class or trying to bunk the class. In case of further exceptionsin the time stamp will be scope of further development of the system. All queries or conditions of the students will be answered by the system on communication with the admin. If the admin finds that the system was at fault then it can always be fixed by the admin for smooth functioning of the class monitoring system.

References

[1] Face detection in colour images Rein-Lein Hsu, Student member IEEE, Mohamed Abdel Mottaleb, Member, IEEE and AnilK.Jain, Fellow, IEEE

[2] Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning Hoo-Chang Shin, Member, IEEE, Holger R. Roth, Mingchen Gao, Le Lu, Senior Member, IEEE, Ziyue Xu , Isabella Nogues, Jianhua Yao, Daniel Mollura, and Ronald M. Summers*

[3] Python.org& Opencv.org

[4] Andrew Ng Machine Learning

[5] Udacity Nanodegree Machine Learning Foundation

[6] P Viola, M. Jones ,Robust, â€Real time object detectionâ€, Compaque CRL,2001

[7] Philip Ian Willson, John Fernandez “Facial Feature Detection using Haar Classifiersâ€, Journal of Computing Sciences in colleges, Vol.21,no.4,pp.127-133,April 2006.

[8] https://developer.nvidia.com/cuda-zone

[9] https://developer.nvidia.com/cuda/cudnn

[10] https://www.tensorflow.org/

[11] Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition Xi Yin and XiaomingLiu, Member, IEEE

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How to Cite

Vikram Bhattacharya, A., Khanna, M., Tripathi, A., & Murugaveni, S. (2018). Class Monitoring System Tools MTCNN and Haarcascade Classifier. International Journal of Engineering & Technology, 7(3.12), 951–954. https://doi.org/10.14419/ijet.v7i3.12.17609

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Received 2018-08-16
Accepted 2018-08-16
Published 2018-07-20