This document presents a project on a face recognition system. It provides an abstract describing the use of biometric security systems like face detection and recognition to provide verification and identification capabilities. It then outlines the various sections that will be included in the report, such as introduction, methodology, tools/technologies, applications and future scope. The methodology section describes using an Agile development approach and details the requirements analysis, data modeling, and process modeling steps. Computer vision, image processing and machine learning tools and technologies are also listed.

1. FACE RECOGNITION SYSTEM
Presented
by:
Ankit Kumar Singh (DDU8362000009)
Ankita Kumari (DDU8362000010)
Kumari Poonam (DDU8362000032)
Under the Supervision of:
Mr. Anshuman Srivastava(Assistant Professor)
Submitted to:
DEPARTMENT OF BACHELOR OF COMPUTER APPLICATION
ITM COLLEGE OF MANAGEMENT
GIDA, GORAKHPUR- 273209
DEENDAYAL UPADHAYAY UNIVERSITY
OF GORAKHPUR-273009

2. CONTENT
• Abstract
• Introduction
• Biometric
• Face recognition
• Problem Statement
• Objectives
• Methodology
• Tools and technologies
• Testing / Experimentation
• Applications
• Future scope
• Conclusion
• References

3. ABSTRACT
 Security has become a major issue globally and in order to manage the security challenges and
reduce the security risks in the world, biometric systems such as face detection and recognition
systems have been built.
 These systems are capable of providing biometric security, crime prevention and video surveillance
services because of their inbuilt verification and identification capabilities.
 This has become possible due to technological advancement in the fields of automated face
analysis, machine learning and pattern recognition. In this research paper, we review some advance
biometric and facial recognition techniques.
 For this, Viola-Jones Algorithm, Local Binary Pattern Histogram Algorithm and Neural Network
Plays a major role. There are lot of machine learning and image processing library.
 This report is mainly based on OpenCV and NumPy i.e., a model is created using these libraries.
To create model, dataset (labelled data) is required which is generated using OpenCV and for
visualization of output, another dataset (without labelling) is generated and this time, a model
predicted the label (i.e., name) of that image.
 The author of this project implemented this whole stuff in GUI (Graphical User Interface) using
Tkinter and the accuracy is found to be very high i.e., it almost predicts all images correctly. The
area of this project face recognition system is Image processing

4. INTRODUCTION
 Face recognition has gained tremendous attention over the last three
decades since it is considered a simplified image analysis and
pattern recognition application.
 There are at least two reasons for understanding this trend: (1) the
large variety of commercial and legal requests, besides (2) the
availability of the relevant technologies (e.g., smartphones, digital
cameras, GPU, …).
 Although the existing machine learning/recognition systems have
achieved some degree of maturity, their performance is limited to
the conditions imposed in real-world applications.
 Today facial recognition, associated with artificial intelligence
techniques, enables a person to be identified from his face or
verified as what he claims to be. Facial recognition can analyze
facial features and other biometric details, such as the eyes, and
compare them with photographs or videos.

5. BIOMETRIC
 Biometrics is the measurement and statistical analysis of people's unique physical and behavioral
characteristics. The technology is mainly used for identification and access control or for identifying
individuals who are under surveillance.
 The basic premise of biometric authentication is that every person can be accurately identified by
intrinsic physical or behavioral traits. The term biometrics is derived from the Greek word’s bio,
meaning life, and metric, meaning to measure.
• Components of biometric devices include the following:
 A reader or scanning device to record the biometric factor being authenticated;
 software to convert the scanned biometric data into a standardized digital format and to compare
match points of the observed data with stored data; and a database to securely store biometric data
for comparison.
 Biometric data may be held in a centralized database, although modern biometric implementations
often depend instead on gathering biometric data locally and then cryptographically hashing it so
that authentication or identification can be accomplished without direct access to the biometric data
itself.

6. • Types of biometrics:
The two main types of biometric identifiers are either
physiological characteristics or behavioral characteristics.
facial recognition
fingerprints
finger geometry (the size and position of fingers)
iris recognition
vein recognition
retina scanning
voice recognition
DNA (deoxyribonucleic acid) matching
digital signatures

7. FACE RECOGNITION
 Face recognition has progressed from rudimentary computer vision
techniques to advances in machine learning to increasingly
sophisticated neural networks and related technologies;
 In engineering, the issue of automated face recognition includes three
key steps: (1) approximate face detection and normalization, (2)
extraction of features and accurate face normalization, and (3)
classification (verification or identification).
 Face detection is the first step in the automated face recognition
system. It usually determines whether or not an image includes a
face(s). If it does, its function is to trace one or several face locations
in the picture.
 Feature extraction step consists of extracting from the detected face a
feature vector named the signature, which must be enough to represent
a face. The individuality of the face and the property of distinguishing
between two separate persons must be checked. It should be noted that
the face detection stage can accomplish this process.
 Classification involves verification and identification. Verification
requires matching one face to another to authorize access to a
requested identity. However, identification compares a face to
several other faces that are given with several possibilities to find
the face’s identity.

8. PROBLEM STATEMENT
 The Problem statement of Face Recognition for Real-Time Applications are given below:
- To do face detection and recognition in real time.
- Enhance the Speed i.e., frames/sec.
- Do recognition on high Camera resolution.
There might have been number of situation where it is necessary to recognize face or simply
detect face. The traditional methods of lock/unlock are very inefficient. There may be possible
of losing keys or breaching of codes/passwords. So, we propose a face recognition system which
can be able to recognize face with maximum accuracy as possible.

9. OBJECTIVES
• The objectives of this system are as follows:
 Detect faces.
 Match detected faces to the images previously captured and recognize them.
 Provides accurate information about them (e.g., their names).
 To enhance the Frame/sec for Face Recognition System, such that Recognition is done in Real
Time.
 Presently, work on 15 frames/sec Our motto is to achieve higher frames/sec or high-Resolution
frames/sec.
 We want increase accuracy of face detection and recognition

10. METHODOLOGY
For this project we will be using the Agile Software
Development methodology approach in developing the
application.
Agile methodologies are approaches to product development
that are aligned with the values ​​and principles described in
the Agile Manifesto for software development.
Agile methodologies aim to deliver the right product, with
incremental and frequent delivery of small chunks of
functionality, through small cross-functional self-organizing
teams, enabling frequent customer feedback and course
correction as needed.
In doing so, Agile aims to right the challenges faced by the
traditional “waterfall” approaches of delivering large products in
long periods of time, during which customer requirements
frequently changed, resulting in the wrong products being
delivered.

11. • Requirement analysis
 Requirement analysis is a process of precisely identifying, defining, and documenting the various requirements that
are related to a particular business objective.
 The functional, non-functional and technical requirements for this project are
• Functional requirements
 It should be able to handle ‘png’ and ‘jpeg’ images.
 It should generate the dataset properly.
 It should be able to predict the authorized users with high accuracy.
• Non-functional requirements
 The GUI of the system will be user friendly.
 The system will be flexible to changes, e.g., an authorized user can be added at any time.
 Efficiency and effectiveness of the system will be made sure.
• Technical requirements
 camera integrated system
 4 GB RAM (Minimum)
 100 GB HDD.
 intel core i3 processor
 Microsoft Windows 10/11.
 MySQL database (MySQL workbench)
 Python programming language ( Version 3.8.5)
 Microsoft Visual studio

12. • Data modeling

13. • Process modeling

15. TOOLS AND TECHNOLOGIES
• The proposed system majorly focuses on the use of these main technologies. These technologies can be
categorized as the following module:
 Computer vision
 Image processing
 Machine learning
• Computer vision
 Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human
vision system and enabling computers to identify and process objects in images and videos in the same way that
humans do. Until recently, computer vision only worked in limited capacity.
 Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has
been able to take great leaps in recent years and has been able to surpass humans in some tasks related to detecting
and labeling objects.

16. • Image Processing
 Image processing is the process of transforming an image into
a digital form and performing certain operations to get some
useful information from it.
 The image processing system usually treats all images as 2D
signals when applying certain predetermined signal processing
methods.
• Machine learning
 Basically, it’s a class of algorithms which tells what the good
answer is. A machine learning algorithm would learn-by-
example or data set which you have provided to your
machine.
 For eg, you’ll show several images of faces and not-faces the
algorithm will learn and be able to predict whether the image
is a face or not. This particular example of face detection is
supervised.

17. • Libraries And Framework Used
 in this project, we have performed face detection and recognition by using OpenCV and NumPy and also use some
other libraries for other image processing operation.
• OpenCV
 OpenCV is a huge open-source library for computer vision, machine learning, and image processing. OpenCV
supports a wide variety of programming languages like Python, C++, Java, etc.
• NumPy
 NumPy is a Python library used for working with arrays.
 It also has functions for working in domain of linear algebra, fourier transform, and matrices.
 NumPy was created in 2005 by Travis Oliphant.
• Pillow
 Python Imaging Library is a free and open-source additional library for the Python programming language that adds
support for opening, manipulating, and saving many different image file formats.
• Tkinter
 Python provides the standard library Tkinter for creating the graphical user interface for desktop based applications.

18. • Machine Learning Algorithms
 For this project, Viola-Jones Algorithm, Local Binary Pattern Histogram Algorithm Plays a major role.
• Viola-jones algorithm
 Face detection is a fundamental part of facial recognition. Before your system can recognize a face, it must detect it
in the image.
 The Viola-Jones Object Detection Framework provides fast techniques for face detection algorithms.
 It is an Object Detection Algorithm used to identify faces in an image or a real time video. The algorithm uses edge
or line detection features
 The detection is performed in real time by analyzing the pixels in photo images of full frontal faces.
High detection rate (Not perfect)
Can distinguish faces from non-faces from arbitrary images
Low false positives, Higher true positives
Applicable in real-time

19. • The Viola Jones algorithm has four main steps
 Selecting Haar-like features
 Creating an integral image
 Running AdaBoost training
 Creating classifier cascades

20. • Local Binary Pattern Histogram Algorithm
 The Local Binary Pattern Histogram (LBPH) algorithm is a face recognition algorithm based on a local binary
operator, designed to recognize both the side and front face of a human.
• Local Binary Pattern (LBP) is a simple yet very efficient texture operator which labels the pixels of an image by
thresholding the neighborhood of each pixel and considers the result as a binary number.
Now that we know a little more about face recognition and the LBPH, let’s go further and see the
steps of the algorithm:
Parameters
Training the Algorithm
Applying the LBP operation
Extracting the Histograms
Performing the face recognition

21. TESTING
 In our software project we perform functional testing operation, functional testing is a part of manual
testing;
1. Unite Testing
Unit testing is the first level of functional testing in order to test any software. In this, the test engineer will test the
module of an application independently or test all the module functionality is called unit testing.
The primary objective of executing the unit testing is to confirm the unit components with their performance.
2. Integration Testing
Once we are successfully implementing the unit testing, we will go integration testing. It is the second level of
functional testing, where we test the data flow between dependent modules or interface between two features is
called integration testing.
3. System testing
Whenever we are done with the unit and integration testing, we can proceed with the system testing.
In this type of testing, we will undergo each attribute of the software and test if the end feature works according to
the business requirement. And analysis the software product as a complete system.

22. APPLICATION

23. FUTURE SCOPE
 Our current recognition system acquires images from file located Database and from webcam. Scanner support can
be implemented for greater flexibility.
 Currently, our system fails under the vastly varying conditions which we can solve in the future.
What the Future Holds?
The future of facial recognition technology is bright. Forecasters opine that this technology is expected to grow at a
formidable rate and will generate huge revenues in the coming years. Security and surveillances are the major
segments which will be deeply influenced. Other areas that are now welcoming it with open arms are private
industries, public buildings, and schools.
It is estimated that it will also be adopted by retailers and banking systems in coming years to keep fraud in
debit/credit card purchases and payment especially the ones that are online.
This technology would fill in the loopholes of largely prevalent inadequate password system. In the long run, robots
using facial recognition technology may also come to foray. They can be helpful in completing the tasks that are
impractical or difficult for human beings to complete.

24. CONCLUSION
 In this research paper we used viola jones and local binary pattern histogram machine learning algorithms and
many libraries and frameworks and we got very great efficiency and better accuracy but also many disadvantages
with viola jones machine learning algorithm, in future we can try to slove this problem and make a advance facial
recognition system.
 Face recognition system recognize the face of authorized users very easily. Those persons who want to use Face
recognition system doesn’t have to know how to make the system but it is sufficient to know how to use it only.
 The main steps of this project are concluded below:
 Step 1: To generate the dataset of authorized users
 Step 2: Use that dataset to train the model
 Step 3: Calculate the accuracy
 Step 4: Use that trained model to predict detected faces
 Step 5: Representing the project into GUI
 All the above-mentioned steps are accomplished successfully. It met our initial aims and objectives and as
mentioned in the limitation, we are working to deal with this too.

25. REFERENCES
 www.w3scools.com
 www.javapoint.com
 www.technopedia.com
 www.Wikipedia.com
 www.techtarget.com