Anti-theft Security System Using Face Recognition
By
Author
Presented To
Department of Computer Engineering
ABSTRACT
This project is about an anti-theft security system using face recognition based on
Raspberry pi. Anti-theft security system has been launched for many years but most of
them are just a CCTV, IP camera or door sensor alert system. It could be more efficient
with uses of face recognition. The design of anti-theft security system is based on human
face recognition and remote monitoring technology. It will verify the person identity that
goes near to the camera within certain distance. Only the people who match the identity
with database and key in the correct password have the right to entry. A mobile
application telegram and image processing technique LBP based have been involved in
this system. Electric door lock solenoid, keypad and LCD display will combined operate
the accessibility of door. An ultrasonic sensor is used to detect the person distance
between the door. Face recognition will only start to function when someone goes near
the door within certain distance. An alert message and photo taken will send to owner
mobile phone via telegram with WIFI when the face recognition algorithms detect a
stranger comes near the door and also a buzzer will be trigger to act as an alarm system.
Owner can control the door locker and check the camera with the telegram application.
The security system is designed for actual use in home condition with the uses of a
microcontroller ARM Quad Cortex-A53.
TABLE OF CONTENTS
Table of Contents
Title i
Declaration of originality ii
Acknowledgements iii
Abstract iv
Table of Contents v
List of Tables viii
List of Figures viii
List of Abbreviations xi
Chapter 1: Introduction
1.1 Problem statement and motivation 1
1.2 Project Scope 1
1.3 Project Objective 2
1.4 Impact, Significance and Contribution 2
1.5 Background information 3
1.6 Achievement 4
1.7 Report Organization 5
Chapter 2: Literature Review
2.1 Discussion on other research 6
2.2 Critical Remarks of previous works 9
Chapter 3: System Design
3.1 Description of Project 11
3.2 Process Flow of System 15
3.2.1 Raspberry Pi 15
3.2.2 Telegram 15
3.3 System Setup Procedures 15
3.3.1 Raspbian OS 15
3.3.2 Telegram 16
3.3.2 VNC Viewer 17
3.4 Software Development 18
3.4.1 Process of Face Recognition 18
3.4.2 Process of Training Dataset 20
3.4.3 Process of Prepare Face Dataset 22
Chapter 4: Design Specification
4.1 Methodologies 24
4.1.1 System Development Methodology 24
4.1.2 Project Flow 25
4.2 Tools 26
4.2.1 System Hardware 27
4.2.1.1 Raspberry pi 3 model B 27
4.2.1.2 Raspberry pi camera module 29
4.2.1.3 I2C LCD1602 display 30
4.2.1.4 Buzzer 31
4.2.1.5 Solenoid locker 31
4.2.1.6 HC-SR04 Ultrasonic Sensor 32
4.2.2 System Software 33
4.2.2.1 Raspbian OS 33
4.2.2.2 Python IDE 33
4.2.2.3 Blynk 34
4.2.2.4 Telegram 34
Chapter 5: Implementation and Testing
5.1 Testing Face Recognition 35
5.2 Testing System 36
5.2.1 Security System 36
5.2.2 Surveillance System 37
5.2.3 Telegram Control 38
5.2.4 LCD Display 40
5.2.5 Blynk 41
5.3 Implementation 42
Chapter 6: Conclusion 43
6.1 Project Review, Discussion and Conclusions 43
6.2 Future work 44
Bibliography 45
Appendices 46
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