Visual system for student attendance monitoring with non-standard situation detection
Authors:
- 1 December 2014
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 221-226
Abstract
In this paper we propose a visual system for monitoring of student attendance in seminars and lectures. Basic idea is to estimate the number of people in the room using face detection algorithms and subsequently utilize face recognition algorithms to determine the actual identification of persons (students). Presented approach may be used for multiple purposes. Principal and primary purpose is to monitor attendance, which is possible thanks to university database. When implemented, system is expected to evaluate the attendance automatically or if necessary using collaborative authentication. Non-standard or anomaly detection is another feature that is to be provided by system, subject to tracking are hands, eyes and movement. Proposed solution is expected to improve and facilitate attendance monitoring of students at seminars and lectures. Further it may be used for anomaly prevention (e.g. cheating) and in specific cases for security or legal matters.Keywords
This publication has 12 references indexed in Scilit:
- Computer vision based object recognition principles in education2013
- MITSAT An automated student attendance tracking system using Bluetooth and EyeOS2013
- Web-based student attendance system using RFID technology2012
- Using artificial neural network for human age estimation based on facial images2012
- Face Recognition Based on Principle Component Analysis and Support Vector Machine2011
- A comparative analysis of different neural networks for face recognition using principal component analysis, wavelets and efficient variable learning rate2010
- Multimodal Biometrics-based Student Attendance Measurement in Learning Management Systems2009
- Automating a student class attendance register using radio frequency identification in South Africa2007
- Histograms of Oriented Gradients for Human Detection2005
- Hierarchical Part-Based Visual Object Categorization2005