report technical writing andpresentation topic face recognition using computervision for security system

espe-to the forenamed matter is a face recognition system using computer vision for the curity system, which can descry intruders in defined or high- security areas and helpminimize mort

HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY

SCHOOL OF ELECTRICAL & ELECTRONIC ENGINEERING

REPORT

TECHNICAL WRITING AND

PRESENTATION

Topic:

FACE RECOGNITION USING

COMPUTERVISION FOR SECURITY SYSTEM

Student: VU MINH HUY

Student ID: 20203721

Class: ET 05 - K65

Supervisor: NGUYEN TIEN HOA, Ph.D

Ha Noi, December 29, 2022

TABLE OF CONTENTS

2.1 Fundamental of deep learning 2

2.2 CNN 2

2.3 Face recognition component 3

2.3.1 Principal Component Analysis 3

2.3.2 Classification of face images 4

2.3.3 Anti-spoofing approach 4

2.3.4 HAAR fopr face detection 4

CHAPTER 3 APPLICATIONS OF FACE RECOGNITION IN COMMON SECURITY SYSTEM 6 3.1 Allications and example 6

3.1.1 Face Identification 6

3.1.2 Access Control 6

3.1.3 Security 7

CHAPTER 4 ADVANTAGES AND DISADVANTAGES OF EACH FA-CIAL RECOGNITION SECURITY SYSTEM 8 4.2 Advantages 8

4.2.1 Security Through Biometric Authentication 8

4.2.2 Automated Image Recognition 8

4.2.4 Human-Computer Interaction 9

4.3 Disadvantages and limitations of facial recognition system 9

4.3.1 Issues About Reliability and Efficiency 9

4.3.2 Further Reports About It Reliability 9

4.3.3 Concerns About Racial Bias 9

4.3.4 Issues with Privacy Laws 9

LIST OF SIGNS AND ABBREVIATIOMS

LIST OF FIGURES

Hình 2.1 A Venn diagram describing deep learning as a subfield of machine

learning which is in turn a subfield of artificial intelligence 3

Hình 2.2 The general idea of the face recognition system 3

Hình 2.3 The detailed procedure of PCA algorithm 4

Hình 4.1 the implementation of the real-time 8

Along with the growth of computer vision over the last numerous decades Fueled

by the steady face recognition have increased at a rate of doubling every 13 months[ ].?

Institutions are now facing major security issues; consequently, they need several espe-cially trained labor force to attain the asked security These labor force, as mortal beings, make misapprehensions that might affect the position of security [?] A proposed result

to the forenamed matter is a face recognition system using computer vision for the se-curity system, which can descry intruders in defined or high- sese-curity areas and help minimize mortal error A face recognition system is the verification system to find a person’s identity through biometric system Face recognition has come a popular sys-tem presently for multitudinous operations, analogous as phone unlock syssys-tems, lawless identification, and indeed home security systems Because of general curiosity and in-terest in the matter, the author has excavated to find out how machine vision recognizes humans and its operations in common security systems

CHAPTER 1 INTRODUCTION

Face recognition presents a challenging problem in the field of image analysis and computer vision Facial recognition application is utilized for safety purposes Govern-ing buildGovern-ings and businesses alike harness the force for thwartGovern-ing crime and providGovern-ing just trusted employees on their assumptions Combining that with the efficient safety team keeps in terms of business finances, and creates a highly operating security sys-tem However, using the technology for nothing but protection could be silly, as it will do

a lot more Facial recognition technology is quickly spreading, as it is starting to follow areas like education, family safety, and personal industries The failure of credit card data and social security figures led to a broad fear, and had millions of people doubting contemporary cyber security practices Facial recognition is the most guaranteed way to keep this from occurring again

This report contains five sections:

• The first chapter describes the problem are commonly security system facing now

• Chapter 2 presents an overview of the face recognition system

• The third chapter depicts applications of face recognition in common security sys-tem

• Finally, the last chapter presents the advantages and disadvantages of each facial recognition security system

CHAPTER 2 OVERVIEW OF FACE RECOGNITION SYSTEM.

Face recognition is part of computer vision Face recognition[1] is used to identi-fying a person in biometric method based on image on their face A person is identified through biological traits Human eyes can easily recognize people by simply looking at them but the concentration span for human eyes has its limit Hence, a computerized method is invented to perform face recognition Face recognition [2] includes the op-erations of automatically etecting followed by verifying a person from either picture or video

2.1 Fundamental of deep learning

Deep learning is a subfield of machine learning, which is, in turn, a subfield of artificial intelligence (AI) 2.1 The central goal of AI is to provide a set of algorithms and techniques that can be used to solve problems that humans perform intuitively and near automatically, but are otherwise very challenging for computers A great example of such a class of AI problems is interpreting and understanding the contents of an image-this task is something that a human can do with little-to-no effort, but it has proven to be extremely difficult for machines to accomplish While AI embodies a large, diverse set

of work related to automatic machine reasoning (inference, planning, heuristic, etc.) the machine learning subfield tends to be specifically interested in pattern recognition and learning from data

Artificial Neural Networks (ANNs) are a class of machine learning algorithms that learn from data and specialize in pattern recognition, inspired by the structure and function of the brain As I’ll find out, deep learning belongs to the family of ANN algorithms, and in most cases, the two terms can be used interchangeably In fact, you may be surprised to learn that the deep learning field has been around for over 60 years, going by different names and incarnations based on research trends, available hardware and datasets, and popular options of prominent researchers at the time

2.2 CNN

A Convolutional Neural Network (CNN) is a type if (ANNs) used in image recog-nition and processing that is specifically designed to process pixel data CNNs are pow-erful image processing, artificial intelligence (AI) that use deep learning to perform both generative and descriptive tasks, often using machine vison that includes image and video recognition, along with recommender systems and natural language process-ing (NLP)

Hình 2.1 A Venn diagram describing deep learning as a subfield of machine learning which is in turn a subfield of artificial intelligence

2.3 Face recognition component

In the facial recognition component, the input face image is tested with the train-ing image in the database Both traintrain-ing image and test image goes through the prin-ciple component analysis (PCA) feature extraction, which is then classified using the Euclidean distance classifier.2.2

Hình 2.2 The general idea of the face recognition system

2.3.1 Principal Component Analysis

For feature extraction, PCA is chosen because it is good at reduction of dimension where data is compressed By representing high dimension data of face images with lower dimension data, it reduces the complication and intricacy of sorting the images in groups PCA defines original data with calculated eigenvectors and eigenvalues when it

is projected onto a lower dimensional feature space PCA captures the most distinctive data component from the face image which helps in maximising between-class data separation[3] The detailed procedure of PCA algorithm is described in 2.3

Hình 2.3 The detailed procedure of PCA algorithm

2.3.2 Classification of face images

In this project, classification is performed by comparing the projection vectors of the training face images with the projection vector of the input face image based on the Euclidean distance classifiers between the faces classes and the input face image The classifier computes the square root of distance between the coordinates of two objects 2.3.3 Anti-spoofing approach

The anti-spoofing approach proposed in this system is by detecting blinking eyes Eye blinking is detected from analysing one by one each sequence image to classify the state of either opened eye or closed eye The eye area is detected by Viola-Jones method, while the blinking of eyes are detected using Histograms of Oriented Gradients (HOG)

as features and Support Vector Machines (SVM) as binary classifiers

2.3.4 HAAR fopr face detection

For detecting the face, the HAAR Cascade algorithm is used in this paper HAAR cascade is one of the deep learning algorithms which can associate things It is found

on the theory presented by PaulViola also Micheal Jones in their paper "Rapid Object Detection with a Boosted Cascade of SimpleFeatures."Thus, popularly known as the Viola-Jones algorithm It is based on a resemblance where acascade function is in-structed using to a great extent positive images also negative images It also associates the things in many images It has four stages: The algorithm has four stages:

• 1.Selection of HAAR features

• 2.Creation of Integral Images

• 3.Adaboost Training

• 4.Cascading Classifiers

CHAPTER 3 APPLICATIONS OF FACE RECOGNITION IN

COMMON SECURITY SYSTEM

Facial recognition is everywhere What once started as an attribute specific to scifi movies is now a part of everyday life: we rely on facial recognition every time we unlock our phones(FACE ID), tag friends in a Facebook post, or go through customs

Face regconition is also useful in human computer interaction, virtual reality, database recovery, multimedia, computer entertainment information security e.g operating sys-tem, medical records, online banking, Biometric e.g Personal Identification - Passports[4]

3.1 Allications and example

3.1.1 Face Identification

Face recognition systems identify people by their face images Face recognition systems establish the presence of an authorized person rather than just checking whether

a valid identification (ID) or key is being used or whether the user knows the secret personal identification numbers (Pins) or passwords The following are example

To eliminate duplicates in a nationwide voter registration system because there are cases where the same person was assigned more than one identification number The face recognition system directly compares the face images of the voters and does not use

ID numbers to differentiate one from the others When the top two matched faces are highly similar to the query face image, manual review is required to make sure they are indeed different persons so as to eliminate duplicates

3.1.2 Access Control

In many of the access control applications, such as office access or computer lo-gon, the size of the group of people that need to be recognized is relatively small The face pictures are also caught under natural conditions, such as frontal faces and indoor illumination The face recognition system of this application can achieve high accuracy without much co-operation from user The following are the example Face recognition technology is used to monitor continuously who is in front of a computer terminal It allows the user to leave the terminal without closing files and logging out When the user leaves for a predetermined time, a screen saver covers up the work and disables the mouse & keyboard When the user comes back and is recognized, the screen saver clears and the previous session appears as it was left Any other user who tries to logon without authorization is denied

3.1.3 Security

Today more than ever, security is a primary concern at airports and for airline staff office, passengers and family Airport protection systems that use face recognition technology have been implemented at many airports around the world Many families use facial security systems to protect themselves and their children

CHAPTER 4 ADVANTAGES AND DISADVANTAGES OF EACH FACIAL RECOGNITION SECURITY SYSTEM

4.2 Advantages

Hình 4.1 the implementation of the real-time

Central to the advantage of facial recognition is that it enables the computerized and automated processing of biometric data based on the digital image or live video feed

of a person for a variety of purposes or applications4.1

4.2.1 Security Through Biometric Authentication

One of the benefits of facial recognition system centers on its application in biomet-rics It can be used as a part of identification and access control systems in organizations,

as well as personal devices, such as in the case of smartphones

4.2.2 Automated Image Recognition

The system can also be used to enable automated image recognition capabilities Consider Facebook as an example Through machine learning and Big Data analytics,

or tagging to individual user profiles.

4.2.3 Deployment in Security Measures

Similar to biometric application and automated image recognition, another advan-tage of facial recognition system involves its application in law enforcement and security systems Automated biometric identity allows less intrusive monitoring and mass iden-tification

4.2.4 Human-Computer Interaction

The system also supports virtual reality and augmented reality applications Filters

in Snapchat and Instagram use both AR and facial recognition In both VR and AR applications, the system facilitates further human-computer interaction

4.3 Disadvantages and limitations of facial recognition system

Despite the advantages and application, facial recognition system has drawbacks and limitations revolving around concerns over its effectiveness and controversial appli-cations Take note of the following:

4.3.1 Issues About Reliability and Efficiency

A notable disadvantage of facial recognition system is that it is less reliable and efficient than other biometric systems such as fingerprint Factors such as illumination, expression, and image or video quality, as well as software and hardware capabilities, can affect the performance of the system

4.3.2 Further Reports About It Reliability

Several reports have pointed out the ineffectiveness of some systems For example,

a report by an advocacy organization noted that the systems used by law enforcement agencies in the U.K had an accuracy rate of only 2 percent Applications in London and Tampa, Florida did not result in better law enforcement according to another report 4.3.3 Concerns About Racial Bias

A study by the American Civil Liberties Union revealed that the Rekognition tech-nology developed by Amazon failed nearly 40 percent false matches in tests involving people of color In general, the system has been criticized for perpetuating racial bias due to false matches

4.3.4 Issues with Privacy Laws

Alleged conflict with privacy rights is another disadvantage of facial recognition

In Illinois, for example, its Biometric Information Privacy Act requires affirmative