Face Recognition based Attendance System using CNN and Haar Classifier

Syed Muhammad Rafi; Shahzad Nasim; Mohsin Khan; Sheikh Muhammad Munaf; Abdul Kabeer Kazi; Muhammad Ashraf

Author(s)

Syed Muhammad Rafi Ziauddin University,Country : Pakistan

Shahzad Nasim Begum Nusrat Bhutto Women University Country ,Pakistan

Mohsin Khan Khadim Ali Shah Bukhari Institute of Technology (KASBIT) Country , Pakistan

Sheikh Muhammad Munaf Federal Urdu University Country , Pakistan

Abdul Kabeer Kazi Ziauddin University,Country , Pakistan

Muhammad Ashraf Ziauddin University, Pakistan

Abstract

-Pakistan is the 5th country with the most population in the world. With the daily explosion of population and enhancement of mobility, the gridlock of traffic is often seen on the road. The gridlock of traffic reduces the efficiency of daily routine work by putting pressure on account of more fuel consumption and wastage of time. This is caused by signal failures or unprofessional ways of traffic management. Traffic management is the main attention to solve this problem. Many solutions were provided from which the techniques like video data analysis, wireless sensor network, infrared sensors, etc. that somehow solves the problem but it is too costly and time-consuming. The study has the objective to reduce traffic gridlock with the help of Internet of Everything Technology (IOE). IOE is the pattern of sense and response. Object detecting sensors can be used at traffic signals, direction boards, and streetlights to detect the presence of vehicles. RFID can be used to read the unique code from RFID tags attached at vehicles to detect the vehicle type. The data will be stored in the database using a network of connections. The data in graphical form can be perceived by Traffic Inspectors to judge the more gridlocked area and flow of traffic. In case of infringement of traffic rules, a fine will be charged. The engagement of this technique will be reliable, effective, and intelligible.

Publication Details

Page(s) 1-5

DOI DOI not available

Published Journal: Journal of Information & Communication Technology (JICT), Volume: 16, Issue: 1, Year: 2022

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Keywords

Feature Extraction, Deep learning, VGG16, convolution neural network, haar cascade classifier, Attendance System

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