Description

Face recognition and tracking for examination monitoring (digital invigilator) 

Abstract:

In this paper, we propose a system for monitoring students in examinations. Here we use a camera for recognizing and tracking each student, each student we are pre allocated to a particular seat if the student goes beyond this point human invigilator will be alerted, and also faced tracking can be used for detecting any malpractice by any student. Here the camera is mounted on a servo motor to avoid any blind spots. The whole system can be designed and implemented around a raspberry pi embedded computer, this eliminates the use of a PC for running a face recognition program. Here for face detection and recognition python opencv can be used, in which Haar cascade classifier is used for detecting face and Fisher face recognizer is used for face recognition. Servomotor control will be based on the feedback from the camera.   

Existing systems:

• Manual invigilation 

Proposed system:

This system is designed for monitoring students during examinations. This system consists of a raspberry pi for central processing and controlling and a camera mounted on a servo motor. Camera is used for monitoring students through face detection and recognition. Python programming is used for image processing. An alert system can be added to raspberry pi, both local and remote alerts can be added. The local alert can be an alarm system and the remote alert can be an IoT-based message alert system.    

Block diagram:

Block diagram description:

• Raspberry is the central unit it runs the main code for the project
• The camera is interfaced to the raspberry pi through the USB interface
• Python programming is used for accessing the camera for video capture 
• A servomotor is used for rotating the camera to a particular angle
• Servo is interfaced to the raspberry pi through a GPIO pin 
• Here PWM is used for accurate controlling of servo 

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