ABSTRACT
This project
is aimed at developing a computer-based Facial
Recognition System for accurate and secure identification of individuals
within an organization or designated environment. The work seeks to address the
limitations of traditional identification methods such as manual attendance, ID
cards, and biometric fingerprints by introducing a more efficient, contactless,
and tamper-resistant approach. The proposed system eliminates common issues
like impersonation, access breaches, duplication, and data inconsistencies by
using facial features as unique biometric identifiers. The Facial Recognition
System is designed as a user-friendly application that enables real-time face
detection, recognition, and record management. It integrates seamlessly into
existing personnel or security frameworks, allowing for the automated and
centralized management of identity records. By leveraging computer vision and
machine learning techniques, the system provides an accurate, fast, and
cost-effective solution for improving operational security, employee tracking,
and access control across various sectors.
TABLE OF CONTENTS
i TITLE PAGE / COVER PAGE
ii CERTIFICATION
iii DEDICATION
iv ACKNOWLEDGEMENT
v ABSTRACT
CHAPTER
ONE INTRODUCTION
1.0
INTRODUCTION…………………………………………………………………………..….…1
1.1
STATEMENT
OF THE PROBLEM………………………………………………………...……..2
1.2
AIM
AND OBJECTIVES………………………………………………………………….………2
1.3
SIGNIFICANCE
OF THE STUDY………………………………………………………………..3
1.4
SCOPE
OF STUDY……………………………………………………………….…………….…4
1.5
DEFINITION
OF TERMS……………………………………………………………………..…..5
CHAPTER
TWO LITERATURE REVIEW
2.0 BACKGROUND THEORY OF STUDY………………………………………………..………..6
2.1 FACIAL DETECTION……………………………………………………………………..……..7
2.1.1 HOW FACE DETECTION WORKS……………………………………………………………..7
2.1.2 FACIAL ANALYSIS………………………………………………………………………...…….8
2.1.3 FACE MATCHING………………………………………………………………………………..9
2.2 APLICATION OF FACIAL IDENTIFICATION……………………………………………...…10
2.3 ADVANTAGES………………………………………………………………………………..…11
2.4 DISADVANTAGES……………………………………………………………………….……..12
2.5 RELATED WORKS…………………………………………………………………………...…13
2.5.1 FACE DETECTION AND FACE TRACKING…………………………………………….……13
2.5.2 FACE POSITIONING AND ALIGNMENT…………………………………………….……….14
2.5.3 FACE FEATURE EXTRACTION………………………………………………………………..15
2.6 CHALLENGES OF FACE IDENTIFICATION……………………………………………...…..17
CHAPTER
THREE SYSTEM INVESTIGATION AND
ANALYSIS
3.1 BACKGROUND INFORMATION ON CASE STUDY……………………………………...…19
3.2 OPERATION OF EXISTING SYSTEM……………………………………………………..….19
3.3 ALGORITHM SELECTION………………………………………………………………...….19
3.3.1 ALGORITHM CHOICE……………………………………………………………………….…19
3.3.2 TRAINING THE MODEL………………………………………………………………...….….19
3.3.3 MODLEL TESTING…………………………………………………………………………..…20
3.4 METHOD USED………………………………………………………………………………...20
3.5 OUTPUT……………………………………………………………………………………….…21
3.6 SUPPORTING LIBRARIES………………………………………………………………….….21
3.7 SUGGESTED SOLUTIONS TO PROBLEM IDENTIFIED………………………………….…22
CHAPTER
FOUR SYSTEM DEVELOPMENT
4.1 SYSTEM DESIGN…………………………………………………………………………….…23
4.1.1 OUTPUT DESIGN………………………………………………………………………………23
4.1.2 INPUT DESIGN……………………………………………………………………………..…23
4.1.3 PROCESS DESIGN…………………………………………………………………………...23
4.2 SYSTEM IMPLEMENTATION…………………………………………………………………..24
4.2.1 PROGRAM DEVELOPMENT ACTIVITIES…………………………………………….……24
4.2.2 PROGRAM TESTING………………………………………………………………………..30
4.2.3 SYSTEM DEPLOYMENT…………………………………………………………………...31
4.3 SYSTEM DOCUMENTATION…………………………………………………………….……31
4.3.1 FUNCTION OF PROGRAM MODULES……………………………………………….…...31
4.3.2 USER MANUAL……………………………………………………………………………...31
CHAPTER
FIVE - SUMMARY, CONCLUSION AND RECOMMENDATION
5.1 SUMMARY……………………………………………………………………………………..33
5.2 CONCLUSION…………………………………………………………………………….…....33
5.3 RECOMMENDATION………………………………………………………………………..…34
REFERENCES
APPENDICES
CHAPTER
ONE
1.0 INTRODUCTION
Ever since IBM introduced
the first personal computer in 1981, to the .com era in the early 2000s, to the
online shopping trend in the last 10 years, and the Internet of Things today,
computers and information technologies are rapidly integrating into everyday
human life. As the digital world and real world merge more and more together,
accurately and effectively identifying users and improving information security
has become an important research topic. Not only in the civil area, but in
particular since the 9-11 terrorist attacks, governments all over the world
have also made urgent demands on this issue, prompting the development of
emerging identification methods.
Traditional identity
identification technology mainly relies on the individual’s own memory (password,
username, etc.) or foreign objects (ID card, key, etc.). However, whether by
virtue of foreign objects or their own memory, there are serious security risks.
It is not only difficult to regain the original identity material, but also the
identity information is easily acquired by others if the identification items
that prove their identity are stolen or forgotten. As a result, if the identity
is impersonated by others, then there will be serious consequences.
Different from the
traditional identity identification technology, biometrics is the use of the inherent
characteristics of the body for identification, such as fingerprints, irises,
face, and so on. Compared with the traditional identity identification
technology, biological features have many
advantages, such as:
i.
Reproducibility, biological characteristics are born
with, cannot be changed, so it is impossible to copy other people's biological
characteristics.
ii.
Availability, biological features as part of the human
body, are readily available and will never be forgotten.
iii.
Easy to use.
Many biological
characteristics will not require individuals to cooperate with the examination
device.
Based on the above
advantages, biometrics has attracted the attention of major corporations and research
institutes and has successfully replaced traditional identification
technologies in many fields. And with the rapid development of computers and
artificial intelligence, biometrics technology is easy to cooperate with
computers and networks to realize automation management,
and is rapidly
integrating into people's daily lives.
When comparing the
differences between different biometrics, we can see that the cost of facial identification
is low, the acceptance from user is easy, and the acquisition of information is
easy.
Facial identification is
the use of computer vision technology and related algorithms, from pictures or
videos to find faces, and then analyze the identity. In addition, further
analysis of the acquired face may reveal some additional attributes of the
individual, such as gender, age, emotion, etc.
1.1 STATEMENT OF PROBLEM
Technology has shown
immense potential in various applications, from security and surveillance to
human-computer interaction. However, despite significant advancements, several
challenges persist in achieving accurate and reliable facial identification
systems.
Due to the traditional
methods currently utilized for identity verification and access control, organizations
encounter numerous challenges, including inaccuracies, inefficiencies, and
security vulnerabilities. These manual or outdated systems are prone to errors and
can be easily compromised, leading to unauthorized access or misuse. For this
reason, there is a need to devise a more efficient and secure solution by
developing an automated facial recognition system using OpenCV and Python. This
project aims to design and implement a robust system that enhances security and
operational efficiency by accurately identifying individuals based on their facial
features, thereby reducing reliance on manual processes and minimizing the risk
of unauthorized access.
1.2 AIM AND OBJECTIVES
AIM
The primary aim of this research
is to develop a reliable and efficient facial identification system using
Python and OpenCV that can be automated and used enhance the security and
access control
mechanisms within
organizations. The system aims to accurately identify individuals in real time,
eliminating the need for traditional methods such as ID cards or manual
verification processes. This solution will not only increase the speed and
accuracy of identity verification but
also significantly reduce
the risk of unauthorized access, ensuring that organizations maintain a secure
and streamlined operation at a reduced cost and with greater efficiency.
OBJECTIVES
·
Algorithm Development: Design and implement advanced
facial identification algorithms
that
improve accuracy and efficiency compared to existing methods.
·
Datasets Creation: Develop a diverse and representative
facial image dataset to train and evaluate the proposed system.
·
Performance Evaluation: Conduct rigorous testing under
varying conditions to assess the system's accuracy and reliability.
1.3 SIGNIFICANCE OF THE STUDY
The significance of studying
facial identification technology encompasses various dimensions, including its
technological advancements, ethical implications, and applications. This multifaceted
technology has transformed numerous sectors, particularly in security, finance,
and marketing, while also raising critical concerns regarding privacy and
accuracy.
·
Technological
Advancements
The evolution of facial
identification technology is marked by significant advancements:
Modern facial
identification systems utilize convolutional neural networks (CNNs) to Enhance
accuracy and speed in face matching. These advancements have made the technology
is more reliable and applicable across various platforms, including smartphones
and security systems.
Also, the incorporation
of artificial intelligence has enabled real-time processing and improved the
technology's adaptability to different environments and conditions, further
expanding its applications.
·
Ethical and Social
Implications
The use of facial
identification systems can infringe on individuals' privacy rights, leading to
public backlash and legislative scrutiny. Incidents of misuse and unauthorized
surveillance have prompted calls for stricter regulations and even bans in some
jurisdictions.
The technology's
challenges in accurately identifying transgender and non-binary individuals
further complicate its ethical landscape, as it may invalidate personal identities
and expressions.
·
Application of Facial
Identification
Face identification can
be traced back to the sixties and seventies of the last century, and after
decades of twists and turns of development, has matured. The traditional face
detection method relies mainly on the structural features of the face and the color
characteristics of the face.
Some traditional face identification
algorithms identify facial features by extracting landmarks, or features, from
an image of the subject's face.
Personal Identification,
in commercial settings, facial identification facilitates secure access to
facilities and streamlines identity verification processes, particularly in
banking and travel. It allows for contactless authentication, improving user
experience and operational efficiency.
1.4 SCOPE OF THE STUDY
This study focuses on the
development and evaluation of a facial identification system capable of
accurately recognizing individuals across varying conditions, such as changes
in lighting, facial expressions, and aging. Research and implementation of
advanced facial identification algorithms to improve accuracy and efficiency.
Compilation of a diverse facial image dataset to train and test the proposed
system. Comprehensive testing of the system under various conditions to assess
its reliability and accuracy. By focusing on these specific areas, the study
aims to contribute to the advancement of facial identification technology while
maintaining a manageable scope.
1.5 DEFINITION OF TERMS
i.
An algorithm is a step-by-step procedure or set of
rules followed by a computer to solve a problem or perform a task. In facial
identification, algorithms analyze facial images and extract features for
comparison.
ii.
Biometrics: refers to technologies that measure and
analyze physical or behavioral characteristics of an individual for
identification or verification purposes. Facial identification is a subset of
biometrics.
iii.
Datasets: A dataset is a structured collection of data
organized for efficient storage and retrieval. In facial identification, it
stores facial images and corresponding identity information for comparison
purposes.
iv.
Facial features: are the distinctive elements of a
human face that are used for identification. These include eyes, nose, mouth,
chin, cheeks, and their relative positions and shapes.
v.
Facial identification: is a biometric technology that
involves recognizing and verifying an individual based on their facial
features. It employs algorithms to analyze unique characteristics such as eye
shape, nose structure, jawline, and facial proportions to match a person's face
to a stored digital image or datasets.
vi.
facial image: is a digital representation of a
person's face captured by a camera or other imaging device. It serves as input
data for facial identification systems.
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