CHAPTER ONE

INTRODUCTION

1. 1. Background of the Study

As the Internet and web technology is growing faster in this highly IT-enabled world, today’s student generation is getting more attached with the online resources more than the books. Surveillance is one of the important aspects in various fields such as banking sectors, military areas, education, or personal security. Due to ever increasing technology people are relying on advanced technologies for their purposes. Security systems such as CCTV, webcams have proven to be hugely popular for security purposes due to their cost-efficient nature and easy maintenance. Surveillance systems have always been playing a vital role in dealing with the exam mal-practice, burglary cases, theft activities, etc (Nikhil et al., 2018).

Online Examination is the true test of knowledge attained in any educational institution, but the approach that is being used to manage the security of this test of knowledge has made it relatively uncertain that examination is a true test of knowledge. Such examination gives, with sufficient details, a clear analysis about the student’s progress in different areas of concentration. Application of computer to the organization of examination is necessary since computer usage in the management of day-to-day activities of individuals and establishments is on the increase. Adopting the accuracy and eff’4iciency offered by the computer to the invigilation of examination will yield a clean sheet. Thus, it is possible to acquire the true test and analysis of a student skill in an unbiased situation (Akazue et al., 2016).

invigilation/supervision has so many flaws due to inadequate staff and breach on the security and privacy of supervisors/invigilators, it leaves a computer-based supervision system as the only way forward toward ensuring full security of examinations in Nigerian Institutions. Virtual examination supervision system (VESS) is the use of IP-surveillance for the security of examinations against all kinds of cheatings such as an examinee replicating another’s write-up, use of unacceptable materials (cell phone, piece of papers), and forming alliance with examination supervisors to cheat (Mohini et al., 2011).

1.2     Statement of the Problem

Examination malpractice is a dishonest conduct by a candidate prior to examination and through the examination to attain success effortlessly. Examination malpractice is a plague that heralds severe menace for the nation and has constantly remained a bother in the Nigerian educational system. Supervising examination has been manual since the inception in the Nigerian educational system, a surveillance system using a webcam to detect examination mal practices, such as a student using a phone or stepping out of the online examination using webcam surveillance is proposed to assuage the stated problem.

1.3     Aim and Objectives of the Study

The aim of this study is to develop a face monitoring and computer-based testing system using webcam. The objectives of the study are as follows;

1. To Design a face monitoring and computer-based testing system using webcam.
2. To identify the absence of a candidate during exams.
3. To identify the use of mobile gadget by a candidate during examination.
4. To authenticate a candidate identity using webcam in real-time.

1.4     Significance of the Study

This study will serve as an avenue where institutions can supervise candidates seating for exams or interview.

The software is also relevant in the following ways:

1. Enable easy detection of exam mal-practice.
2. Enable the identification of candidate face via webcam
3. Enable the identification of mobile device via webcam
4. Reduce the cost of using physical invigilators to carry out the supervision task.
5. Reduce the time of carrying out examination supervision manually.

1.5     Scope of the Study

The research face monitoring and computer-based testing using webcam is limited to online exams malpractice and detection scenarios only.

1.6   Limitation of the Study

The researcher faced some limitations during the course of this research. Some of this limitations/constraint includes;

i.        Time constraint: Time factor was the major factors as the timeframe to submit the project was too short for a rigorous research.

ii.       Financial constraint:  Lack of adequate finance to fund the researcher as to the purchase of data to review related works online was another major setback.

1.7     Definition of Terms

1. Artificial Intelligence: It is an intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals.
2. Data Argumentation:   Is use in deep learning to regularize, normalize the information on data to be trained.
3. Data Notation:    This is the process of labeling data to make it usable for machine learning.
4. Dataset:      A collection of related sets of information that is composed of separate elements but can be manipulated as a unit by a computer.
5. Deep Learning: It is a part of broader family of machine learning model base on artificial neural networks.

• vi. Examination Malpractice: is defined as a deliberate wrong doing contrary to official examination rules designed to place a candidate at unfair advantage or disadvantage.

7. Examination: A test or examination is an educational assessment intended to measure a test-taker's knowledge, skill, aptitude, physical fitness, or classification in many other topics.
8. Webcam:  webcam is a video camera that feeds or streams an image or video in real time to or through a computer to a computer network, such as the Internet. Webcams are typically small cameras that sit on a desk, attach to a user's monitor, or are built into the hardware.
9. Image:     This refers to the optical counterpart of an object produced by an optical device (such as a lens or mirror) or an electronic device.
10. Machine Learning: It is a scientific study of algorithms and statistical model that computer system uses in other to perform a specific task effectively, without using an explicit instruction relying on patterns and inference instead. It is seen as subset of artificial intelligence.
11. Model: Is a description or analogy used to help visualize something that cannot be directly observed.
12. Multilayer Perceptron: This refers to fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer.
13. Open CV: OpenCV is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez. The library is cross-platform and free for use under the open-source Apache 2 License
14. Perceptron: This refers to a linear classifier (binary) use in supervised learning. It helps to classify the given input data.
15. Software:   It is a program and other operating information use by a computer system.
16. Tensor-Flow: is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.
17. Face Recognition: is a method of identifying or verifying the identity of an individual using their face. Face recognition systems can be used to identify people in photos, video, or in real-time.
18. CCTV: Closed-circuit television, also known as video surveillance, is the use of video cameras to transmit a signal to a specific place, on a limited set of monitors.
19. Surveillance: Surveillance is the monitoring of behavior, activities, or information for the purpose of information gathering, influencing, managing or directing.