Face detection and recognition report

A Project Report On
Face Detection and Recognition Using Raspberry Pi.
Department of Electronics & Communication Engineering.
C. K. Pithawala College of Engineering & Technology
Prepared By:
1) BALSARA DIMPLE. (120090111055)
2) NAIK HETVI. (130090111055)
3) CHAMPANERIA VATSAL. (140093111005)
4) PARMAR KRUNAL (140093111017)
5) RAJ KHUSHBU (140093111033)
Guide: Dr. Mita Paunwala.

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Acknowledgment
We are always keeping our eyes open for the next big opportunity, but the world is now, it will
take enormous resource, both in money and in engineering talent, to make it happen. I dedicate
this project to the Management, all the Faculties, My College Friends and the entire staff of the
“C. K. Pithawalla College of Engineering and Technology”, Surat for being an inspiration and
guide during our Project Work.
It has been a great honor to work, right from the conceptualizing of the entire work of the project,
under the guidance of our respected and honorable guide, Dr. Mita paunwala, in Electronics and
Communication department. We sincerely acknowledge her valuable contribution. She is one of
the constant source of moralization and momentum that any difficulty becomes simple. We gained
a lot of valuable guidance and prompt suggestions from her during the entire schedule; we will be
indebted to her.
Along with her, our Head of Department, Dr. Ninad bhatt, is always been there with open handed
warm help and advices. He even threw lights on some parts like software issues and hardware
alternatives.
We shall indeed be ever grateful for all supports and grateful for Guidance.

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ABSTRACT
Most of world’s government agencies are investing a considerable amount of resources for
improving performance of security systems because recent terrorist attacks, illegal immigrations,
criminal ID verification, failure of verifying person’s identity in uncontrolled environment
exploited dangerous flaws and weaknesses in current safety and security mechanisms. So, these
agencies are now motivated to improve security of systems using physical or behavioural
characteristics of person called Biometrics.
Biometric systems are emerging trends in this world of technological revolution for authentication
purpose of an individual. They are more accurate and secured as compared to token based and
knowledge based identification methods. Till now most of the systems designed for such purposes
involved usage of biometrics traits such as Fingerprint and Iris because they gives better accuracy
but they also suffer from drawbacks as well. Iris recognition systems are highly accurate, reliable
but too intrusive and expensive to implement. Fingerprint recognition system are highly socially
accepted, reliable, non-intrusive but not applicable for non-collaborative people. More than this,
they require much explicit user co-operation. On the contrary, face recognition system represent
good compromise between socially acceptances, reliability, no physical contact required and
balances security and privacy as well. Face recognition systems can be used in various commercial,
security and forensic applications.
A facial recognition system is a computer application for automatically identifying or a verifying
a person from a digital image. One of the ways to do this is by comparing selected facial features
from the image and a facial database through Open Cv. It is Commercial and academic free library
and has advantages over current software that we are using. Face detection algorithms focus on
the detection of frontal human faces. It is analogies to image detection in which the image of person
is matched bit by bit. Image matches with the image stores in database. Any facial feature changes
in database will invalidate the matching process.

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CERTIFICATE
This is to certify that the dissertation entitled “FACE DETECTION AND RECOGNIGATION
WITH RASPBERRY PI” has been carried out by RAJGOR DIMPLE (120090111055), HETVI
NAIK (130090111055), CHAMPANERIA VATSAL (140093111005), PARMAR KRUNAL
(140093111017), RAJ KHUSHBU (140093111033) under my guidance in the partial fulfilment
of the Degree of Bachelor of Engineering in Electronics and Communication (VII semester) of
Gujarat Technological University, Ahmedabad during the Academic year 2016-2017.
Dr. MITA PAUNWALA Dr. Ninad S. Bhatt
(Project Guide) (Head of the Department)
External Examiner

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GUJARAT TECHNOLOGICAL
UNIVERSITY
CERTIFICATE FOR COMPLETION OF ALL ACTIVITIES' AT ONLINE PROJECT
PORTAL B.E. SEMESTER VII, ACADEMIC YEAR 2015-2016
Date of certificate generation: 27 October 2016 (23:53:28)
This is to certify that, Balasara Dimple Mahendrabhai ( Enrolment Number - 120090111055 )
working on project entitled with Face Recognition Using Raspberry Pi from Electronics &
Communication Engineering department of C. K. Pithawalla College Of Engineering &
Technology, Surat had submitted following details at online project portal.
Periodic Progress Reports (PPR) Completed
Design Engineering Canvas (DEC) Completed
Patent Search and Analysis Report (PSAR) Completed
Final Plagiarism Report Completed
Final Project Report Completed
Name of Student: Balasara Dimple Mahendrabhai Name of Guide: Dr.Mita Chirag Paunwal

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Signature of Student: Signature of Guide:
GUJARAT TECHNOLOGICALUNIVERSITY
PORTAL
B.E. SEMESTER VII, ACADEMIC YEAR 2015-2016
Name of Student: Naik Hetvi Vikram Name of Guide: Dr. Mita Chirag Paunwala
This is to certify that, Naik Hetvi Vikram ( Enrolment Number - 130090111055 ) working on
project entitled with Face Recognition Using Raspberry Pi from Electronics &

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GUJARAT TECHNOLOGICAL UNIVERSITY
PORTAL
Name of Student: Champaneria Vatsal Bhadresh Name of Guide: Dr. Mita Chirag Paunwala
This is to certify that, Champaneria Vatsal Bhadresh ( Enrolment Number - 140093111005 )

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PORTAL
This is to certify that, Parmar Krunalkumar Jitendrabhai (Enrolment Number -140093111017)
Name of Student: Parmar Krunalkumar Jitendrabhai Name of Guide: Dr. Mita Chirag Paunwala

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PORTAL
This is to certify that, Raj Khushbuben Harendrasinh ( Enrolment Number - 140093111033 )
Name of Student: Raj Khushbuben Harendrasinh Name of Guide: Dr. Mita Chirag Paunwala

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List of Figures:
FIGURE 1: Plagiarism Report.............................................................................................1
FIGURE 2: Different Biometric Traits................................................................................5
FIGURE 3: Face Detection....................................................................................................6
FIGURE 4: Camera Interfacing With Raspberry..............................................................7
FIGURE 5: AEIOU Summary..............................................................................................10
FIGURE 6: Empathy Mapping.............................................................................................12
FIGURE 7: Ideation Canvas.................................................................................................13
FIGURE 8: Product Development........................................................................................15
FIGURE 9: Function of Face Detection...............................................................................16
FIGURE 10: Downloading OpenCV....................................................................................20
FIGURE 11: Open NewProject in Visual Studio...............................................................20
FIGURE 12: Adding NewProject in Visual Studio............................................................21
FIGURE 13: Edit Additional Include Libraries in Visual Studio.....................................21
FIGURE 14: Adding Additional Directives in Visual Studio............................................21
FIGURE 15: Setup ofPI B3 Model.....................................................................................23
FIGURE 16: Basic Block Diagram ofFace Detection System..........................................24
FIGURE 17: Haar Cascade Feature....................................................................................26
FIGURE 18: Face Detection on Using Haar Cascade........................................................26
FIGURE 19: Overall Interfacing..........................................................................................27
FIGURE 20: Output of Detection.........................................................................................28

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INDEX
ACKNOWLEDGMENT ........................................................................................................................ i
ABSTRACT............................................................................................................................................ ii
CERTIFICATE...................................................................................................................................... iii
LIST OF FIGURE................................................................................................................................. iX
INDEX..................................................................................................................................................... X
PLAGIARISM REPORT......................................................................................................................... Xi
CHAPTER 1. INTRODUCTION.............................................................................................................2
1.1 Problem Statement ..........................................................................................................3
1.2 Aims and Objectives .......................................................................................................3
1.3 Problem Specification ..................................................................................................... 3
1.4 Background......................................................................................................................4
1.4.1 Introduction to biometric traits............................................................................... 4
1.4.2 Factors affecting the selection of a particular biometric........................................ 4
1.4.3 Face as the most attractive biometric.................................................................... 5
1.4.4 Basic mode of face biometric system ....................................................................6
1.4.5 Introduction to raspberry Pi ..................................................................................7
1.4.6 Raspberry pi model B3 as most attractive ............................................................7
1.5 literature review...............................................................................................................8
1.5.1 Face detection using haar feature...........................................................................8
1.5.2 To research and Implementation of the Method of retreating the face image based
on open cv machine visual library ........................................................................8
1.5.3 camera interfacing with raspberry PI .................................................................... 8
1.5.4 Merging open CV with Raspberry PI .................................................................... 9
1.6 Materials and tools required ........................................................................................... 9
1.6.1 Hardware ............................................................................................................... 9
1.6.2 Software ................................................................................................................ 9
CHAPTER 2: Design Methodology..........................................................................................................10
2.1 Design Engineering-Canvas Activity...............................................................................10
2.2.1 AEIOU summary....................................................................................................10
2.2.2 Empathy mapping canvas.......................................................................................12
2.2.3 Ideation canvas........................................................................................................13
2.2.4 Product development ..............................................................................................15
CHAPTER 3: IMPLEMENTATION......................................................................................................18
3.1 Basics of image processing .............................................................................................18
3.1.1 Overview of image ...................................................................................................... 18
3.2 Introduction to Open Cv .................................................................................................18
3.3 Setup Open CV library with msvs2013...........................................................................19
3.3.1 Download Open Cv 3.0 from opencv.org ....................................................................20

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3.3.2 Creating a new project..................................................................................................20
3.4 Introduction to piB3 Model .............................................................................................22
3.5 Setup of Pi B3 model.......................................................................................................23
3.6 Overall Basic block diagram of face recognition system..............................................24
3.7 Face Detection...............................................................................................................25
3.7.1 Haar Features..............................................................................................................25
3.7.2 Face Detection Using Haar Cascad ...........................................................................26
3.7.3 Algorithm for face detection in open CV...................................................................27
3.7.4 Haar cascade with open CV in PI...............................................................................27
3.7.5 Output of Face Detection............................................................................................28
3.7.6 Result...........................................................................................................................28
CHAPTER 4: SUMMARY .....................................................................................................................29
4.1 Summary of Results .......................................................................................................29
4.2 Advantages .....................................................................................................................29
4.3 Scope of future work.......................................................................................................30
REFERENCES........................................................................................................................................31
APPENDIX…………………………………………………………………………………….32

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Plagiarism Report
FIGURE: 1 Plagiarism Report

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CHAPTER 1. INTRODUCTION
Recent activities such as terrorist attacks, illegal immigration exposed serious flaws and
weaknesses in current most of security and safety mechanisms. Most of government agencies are
investing a considerable amount of resources for improving security of systems .Knowledge based
identification systems which involved use of password, personal identification number and Token
based identification systems involving driver’s licence, passport, etc. are not sufficiently secure
because password can be easily stolen and public or private key can be easily modified by some
intruders of system. Even there are lot of cases of passport De-duplication which is a simple way
since a long time for entering into different country or illegal immigration. So, there is strong
requirement of developing security systems which can provide secure way for identifying or
verifying person’s identity for successful communication.
Biometric traits are the unique distinctive measurable characteristics of every individual.
So, researchers are now motivated to improve security of data system based on physical or
behavioural characteristic of person called “Biometrics”. Since long time, the most common
biometrics used for biometric based security systems are iris and fingerprint but many of other
human characteristics have also been studied in last years are finger/palm geometry, voice,
signature, face and gait. Iris recognition systems are highly accurate but expensive to design, not
very much accepted by user and require efficient sensor. On the other side, face recognition system
have high reliability, social acceptability, no physical contact required and balances security and
privacy as well.
Out of the multiple remarkable and stringent abilities of human Vision, “Face recognition”
has been considered as most remarkable ability of human vision. It developed over several years
of childhood, is important for several aspects of our social life and along with this other abilities
such identifying expression of person with we are interacting , has always played as important role
in course of evaluation. The problem of face recognition was considered in early stage of
advancement of computer vision and is now being completely revolutionized over last four
decades. Several efficient techniques and algorithms came into existence which can perform face
recognition using still images from digital camera or video surveillance system. Due to
considerable resources of investigation in the direction of face recognition made it possible to use
“face” as a key biometric in biometric authentication system.

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1.1Problem Statement
Now-a-days security is most required thing. In many methods security is not accurate, systems like
lock key based, password based system. Biometrics gives options to verify based on physical
access control. Face recognition is solution of it. Face recognition won’t require person’s special
attention.
1.2 Aims and Objectives
The project objective is to implement face detection in an optimum way in terms of run time onto
the raspberry pi system. Various algorithms and methodologies are studied and hardware resources
planning will be done to achieve the goal. This kind of face detection system can be widely used
in our daily life in different sectors. We hope that human life can be greatly helped with this
technology.
1.3 Problem Specification
Why open CV and not MATLAB?
 Speed: MATLAB is built on Java, and Java is built upon C. So when you run a
MATLAB program, your computer is busy trying to interpret all that MATLAB code.
Then it turns it into Java, and then finally executes the code. Open CV, on the other hand,
is basically a library of functions written in C/C++.
 Resources needed: Due to the high level nature of MATLAB, it uses a lot of your
systems resources. MATLAB code requires over a gig of RAM to run through video. In
comparison, typical Open CV programs only require ~70mb of RAM to run in real-time.
 Cost: List price for the base (no toolboxes) MATLAB (commercial, single user
License) is around USD 2150. Open CV (BSD license) is free!
 Portability: MATLAB and Open CV run equally well on Windows, Linux and
MacOS. However, when it comes to Open CV, any device that can run C, can, in all
probability, run Open CV.
Why Raspberry Pi and nothing else?
▪ The power consumption: It draws about one tenth of power compare to full size
servers

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▪ No moving parts: pi uses SD card for storage which is fast hence no moving part. No
fansand other things to worry
▪ Small size: Pi with a case can be held in our hand this means pi can be integrated
inside devices.
▪ Status lights: There are several lights mount on pi to have clear case indication
▪ Expansion capability: There are number of devices available for pi. It can be
connected to HDMI inbuilt capable graphics. Number of GPIO pins and two USB
ports.
▪ Cost : Pi of us the best specs for the price
▪ Multiple usage: SD card usage helps to swap with other SD card for quick and easy
change functionality of pi.
1.4 Background
1.4.1 Introduction to biometric traits
Biometric traits are the distinctive measurable characteristics that are unique for every individual.
Due to this, Most of researchers started working on designing recognition system using following
biometric traits. Biometric identifiers are categorized into two characteristics: [1] Physiological
characteristics and are related to the physical parts and shape of the body or the things he possesses
by birth. As for example, fingerprint, face, DNA, palm, hand geometry, iris and retina recognition.
Physiological traits are distinctive and permanent, unless some factors viz. accidents, illnesses,
genetic defects, or aging alters/destroy them. [2] Behavioural characteristics and are related to the
behaviour of a person or the way he usually acts. As for example, typing rhythm, gait, signature,
keyboard stroke and voice. Biometric traits are widely replacing token based identification systems
and knowledge based identification systems because of their uniqueness, social acceptability and
secured characteristics. These traits are used for accessing some restricted areas as in safes created
for keeping some secured materials such as money, maps, antiques, etc. or some secured building
and industries, authentication in day-to-day affairs like dealing with the post office, and detection
of a suspect in a particular crime in the field of criminal investigation. Biometrics that are feasible
for authentication systems are namely: fingerprints, shape and geometry of face, hands, fingers or
ears, the pattern of veins, irises, teeth, as well as samples of DNA (Physiological traits) as well as
gait, voice, keystroke and signature dynamics(Behavioural traits) as shown below. Mostly used
modern technologies employ biometrics such as fingerprints, faces, vein patterns, irises, voices
and keystroke patterns.
1.4.2 Factors affecting the selection of a particular biometric
The selection of a particular biometric depends on the following factors given below:
 Universality: Every individual using this system should possess the trait.

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 Uniqueness: This trait must be distinguishable for each and every individual.
 Permanence: This trait must not change with respect to time.
 Measurability: The database creation is one of the major steps in biometric recognition system.
Hence, it must be easily acquirable and accurate enough for proper recognition operation.
 Performance: Technology that is used for the implementation of this system must be accurate,
speedy and robust in nature.
 Acceptability: Amount of acceptance about the technology among individuals and their
willingness to have their trait being captured and assessed.
 Circumvention: The ease of imitation of biometric trait should be considered because it plays
vital role in recognition applications.
FIGURE: 2 Different Biometrics Traits [8]
1.4.3 Face as the most attractive biometric
Current research in face recognition system is motivated not only by the fundamental challenges
in face recognition systems but also by numerous practical and real time applications where human
identifications needed. Face recognition, as one of the primary biometric applications became
more and more important due to rapid advances in technologies such as digital cameras, internet,
mobile devices, and increased demands on security and safety. In comparison with other
biometrics, the major advantages of face as a key biometric are:

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(1) The processing of face is highly reliable, non-intrusive and no physical co-operation required
that makes the interfacing surface germ free
(2) Large amount of possible availability of unique features
(3) highly socially acceptable because even we can capture face from crowd and it does not require
proper co-operation of individual as in case of fingerprint and iris because Iris recognition systems
require efficient sensor and user co-operation to catch retina images and fingerprint system fails
completely for non-collaborative people.
1.4.4 Basic mode of face biometric system
The face biometric recognition system functions in two modes namely face verification and face
identification. Fig.3 shows concept of face verification and face identification modes of operation
of face recognition system.
Face verification:
In this mode the system compares a captured face image template with a specific template stored
in a face database in order to verify the genuineness of an individual. So, it works as one to one
mapping which checks identity of said person. Initially, reference templates for all the users are
created and enabled to create database. Then, some templates are matched against reference
templates to calculate the genuine and impostor scores and the threshold. Then depending upon
the threshold and scores generated, person is either rejected or accepted.
Face identification:
In this mode the system compares a captured face image template with all the templates stored in
a database in order to identity an unknown individual or unauthorized user. So, it works as one too
many mapping which checks whether person belongs to database templates. If the comparison of
a given template to all templates in the database is within the conformities of the threshold then
the individual's identity can be successfully confirmed.

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FIGURE: 3 Face Detection
1.4.5 Introduction to Raspberry Pi:
New hardware system for human face detection is raspberry pi it is credit card size computer with
components mount on its motherboard, running a dedicated version of windows. It plugs in TV
and keyboard. It is capable to work as small computer which can fit into electronic device.
To perform desktop computer functionality with very low price.
1.4.6 Raspberry Pi model B3 as most attractive:
SoC:Broadcom BCM2837
CPU: 4× ARM Cortex-A53, 1.2GHz
GPU: Broadcom Video Core IV
RAM: 1GB LPDDR2 (900 MHz)
Networking: 10/100 Ethernet, 2.4GHz 802.11n wireless
Bluetooth: Bluetooth 4.1 Classic, Bluetooth Low Energy
Storage: micro SD
GPIO: 40-pin header, populated
Ports: HDMI, 3.5mm analogue audio-video jack, 4× USB 2.0, Ethernet, Camera Serial Interface
(CSI), Display Serial Interface (DSI)

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FIGURE: 4 camera interfacing with Raspberry pi
1.5 literature review:
1.5.1 Face detection using haar-like feature
Face detection from an image has been playing a vital role in the active research area, especially
in computer vision research for more decades beginning from Eigen Faces, Principal Component
Analysis, for numerous applications from monitoring, surveillance, Biometric and Human
Computer Interaction. Face detection proposed by Viola and Jones, Face detection is most used
face detection which is based on statistical methods for rapid frontal Face detection system using
Haar-like features. Analyzing the pixels for face detection is a time consuming and difficult task
to accomplish because of the wide variations of shape and pigmentation within a human face.
Viola and Jones devised an algorithm, called Haar Classifiers, to rapidly detect any object,
including humanfaces, using AdaBoost classifier cascades that are based on Haar-like features and
not pixels .Haar –Like features widely used for identifying and locating human face in images
regardless of size, position and condition including colour, texture and motion under dynamic
environments. And the resources of the systems are considered to be monopolized by face
detection.
1.5.2 The Researchand Implementation of the Methodof Pre-treating the Face Images Based
on Open CV Machine Visual Library:
Two processing algorithms of image enhancement in the process of pre-treating facial recognition,
median filtering and histogram equalization, are comparative analysed in this thesis. In this thesis
the advantages and usage of Open CV machine visual library are analysed, all the algorithms are
come true under the VC++6.0 and MATLAB development environment and are experimented on
the ORL face library. The result proves that the algorithms of processing the face samples in this

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thesis have positive effect, much more pertinence and are suitable to pre-treat the face images.
Open CV machine visual library can be used as a development tool of facial recognition system.
1.5.3 Camera interfacing with raspberry pi:
Raspberry pi camera module can be used to take high definition still photographs open CV and
raspberry pi consist of inbuilt libraries to interface camera by using some simple commands
various effects can be created to an image the pi camera can work with raspberry pi models. It has
command lines for using camera pythons script can used to access camera model directly.
Pi camera implementation is beautiful without knowledge of low level languages or programming
of any embedded electronics raspberry pi can work effortlessly. The camera output can be easily
displayed on monitor using prompt on laptop.
1.5.4 Merging open CV with raspberry pi:
This master thesis presents an approach to count people passing through a virtual gate using a
fixed cheap Picamera mounting vertically on the raspberry Pi board and Python programming
tool linked to the application. The results show that using a camera to count people is good
alternative to other sensors for big entrance because more accurate. But it shows also that the
system needs a lot of improvements to be really reliable.
1.6 Materials and Tools Required
1.6.1 HARDWARE
▪ Pi Camera
▪ Raspberry pi B3 model
1.6.2 SOFTWARE
▪ Open CV 2.4.10
▪ Microsoft visual studio desktop expres

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CHAPTER 2: DESIGN METHODOLOGY
2.1 Design Engineering-Canvas Activity
During semester 7th the we are required to carry out the following design engineering canvas
activity related to our project to identify and good ideas and project definition to work upon
1) AEIOU summary
2) Empathy mapping
3) Ideation
4) Product development
2.1.1 AEIOU summary
We are required to carry out the observation for our selected domain with the help of AEIOU
format. AEIOU is an investigative tool to help a designer to interpret observations gathered by
ethnographic practises. It is an observation tool its two primary functions are to code/gather data
and to develop building blocks of model by analysis of data.
Environment:
FIGURE: 5 Environment
It includes the entire arena and possibilities where the activity can take place which is mentioned
in canvas.
o Cloudy foggy
o Sundown
o Silence of dark

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Interactions:
FIGURE: 6 Interactions
Interactions are between a person and someone. They are building blocks of activities which
included like
o Rich crème people
o Security, Maids
o Vendors, workers
Objects:
FIGURE: 7 Objects
Objects are building blocks of the environment. It include which hardware/ software are used in
this project which includes
o Inquiry Desk, Security Cabins
o Cars, Clubs, Security Office
o Intercom, Lifts

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Activities:
FIGURE: 8 Activities
Activities are goal directed sets of actions towards things people want to accomplish this goal
which will have information such as
o People returning from job
o Kids coming from school, classes
o People, aged citizen gathering in garden and club
o Maids, guards leaving duties
Users:
Figure: 9 Users
Users are the people whose behaviours, needs are being observed which includes
o Security
o Bankers
o Locker holders

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2.1.2 Empathy mapping:
It was our general observation tour in our city. The mapping of our project on basis of daily
routine of our surrounding. The basic problem to be solved through one specific situation is
carried out to be our floor plan.
Users:
Figure: 10 Users
Users are the people whose needs are being observed which includes
o Industrials
o Bankers
o Concerned one for their home
∙Stakeholder:
Figure: 11 Stakeholder
It includes the investors who can have benefit from this idea
o Companies
o Builders
o Business men
Activities:
Figure: 12 Activities
The people doing stuff during our problem scenario.
o Children playing in garden
o Vendors, maids, swappers interacting with society members.
o Visitor registering their identification for entry

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o Senior people having walk
Story Boarding:
Figure: 13 Story Boarding
The actual problem story, which we decide as our base plan is need to be described. It has two
parts: SAD and HAPPY. The conclusion for problem is also derived here.
o SAD: A business man worried for security of his child.
o HAPPY: he found the high tech low cost easy maintain security system,
o CONCULSION: Our base plan to make security evolved.
2.1.3 Ideation:
An ideation canvas is a rough whiteboard/sheet where ideas can be stretched into any limits or
dimensions. Ideation session is not aimed at finding solutions to the defined problem. But its aim
is to define the best possible problem and stretch out its possible scope. The field is set and the
overall agenda is to build the clones of the ideas and pivot them throughout the canvas so as to
discover new possibilities.
People:
Figure: 14 People
This are many persons who can use this system which includes
o Owner of house, kids
o society members
o Workers
o vendors
o Security team

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∙Activities:
Figure: 15 activities
Activities are goal directed sets of actions towards things people want to accomplish this goal
which will have functions for
o Security team to take records
o The entry of known peoples and easy access
o Close watch on exchange of duties of workers
o Door scenario in home with recording
Situation/context/location:
Figure: 16 Situation/context/Location
It gives the information about places or situation where the system can be used
o Crowdie area
o Sundown or foggie time
o High security requirement
o Crime helping
Props/possible solutions:
This will possibly different software and hardware can used.

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Figure: 17 Props/Possible Solutions
o Software:
Open CV
C++ environment
Raspberry Pi python
o As we discuss Open Cv is solution od MATLAB
o Hardware:
Web camera
Raspberry pi B3
Keyboard, mouse, monitor
o We can also use smart phones and sensors.
o As we discuss B3 model is all what we want.
2.1.4 Product Development:
In business and engineering, new product development (NPD) is the complete process of bringing
a new product to market. New product development is described in the literature as the
transformation of a market opportunity into a product available for sale and it can be tangible (that
is, something physical you can touch) or intangible (like a service, experience, or belief). A good
understanding of customer needs and wants, the competitive environment and the nature of the
market represent the top required factors for the success of a new product.
Cost, time and quality are the main variables that drive the customer needs. Aimed at these three
variables, companies develop continuous practices and strategies to better satisfy the customer
requirements and increase their market share by a regular development of new products. There are
many uncertainties and challenges throughout the process which companies must face. The use of
best practices and the elimination of barriers to communication are the main concerns for the
management of NPD process. It also include purpose, production features, production experiment,
list of components, possible props, their respective solutions and alternatives, their situations and
context of working, etc.

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Purpose:
Figure : 18 Purpose
It decides purpose of the project which includes
o Authorization
o Guaranteed data
o Identification Based Physical Factor
∙People:
Figure : 19 People
This are those persons who can use this system which includes
o Trade centre/mall owners
o Control rooms of civilization
o Military or scientist
o Bankers/ jewellery and antiques collector
o Businessmen/ Hotels/ industry

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Product Function:
Figure : 20 Product Function
It will decide how the system will work.
FIGURE: 21 Function of Face detection
● Product features:
Figure: 22 Product Features
It emphasis on features on which the product is designed and it includes
o Store visitor
o Data Memory
o Extension
o No of interfacing

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∙Components:
Figure: 23 Components
It will give description about the hardware and software used in project
Software: open CV 2.4.10
o Software: Visual Studio desktop express
o Hardware: Sensors
o Hardware: Web camera

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CHAPTER 3: IMPLEMENTATION
3.1 Basics of image processing:
Definition:
Image processing is a method of converting an image into its digital form and getting an enhanced
image of the original image by performing some operations on it. It also helps in extracting the
important features of the image for more accurate and informative result. Image processing system
includes treating images as 2D signals while applying set signals processing technique Image
Processing forms core research area within engineering and computer science disciplines too. It is
among rapidly growing technologies today, with its applications in various aspects of a business.
Types:
The two types of methods used for Image Processing
Analog signal processing
 Digital signal processing
3.1.1 Overview of image:
An image may be defined as a two-dimensional function f(x,y), where x and y are spatial (plane)
coordinates, and the amplitude of fat any pair of coordinates is called the intensity of the image at
that point.
3.2 Introduction to Open Cv
Open Cv (Open source Computer Vision) As Open Cv is having BSD (Berkley Source
Distribution) license, it is freely available for both academic and commercial use. C, C++, Python
and Java are the languages that interfaces with Open Cv. It supports Windows, Linux, OS X,
FreeBSD, NetBSD, OpenBSD, and Mobile: Android, iOS, Maemo, BlackBerry 10.OpenCV is
written in C/ C++, so it has advantage of multi-core processing. It can be interfaced with hardware
also by using OpenCL. It is adopted all around the world and having around 47 thousand users.
Open Cv's application areas:
 2D and 3D feature toolkits
 Facial recognition system
 Gesture recognition
 Human–computer interaction (HCI)
 Mobile robotics
 Object identification

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 Segmentation and recognition
 Motion tracking Open Cv has a modular structure, which means that the package includes several
shared or static libraries.
The following modules are available:
 Core - a compact module defining basic data structures, including the dense multidimensional
array Mat and basic functions used by all other modules.
Imporc - an image processing module that includes linear and non-linear image filtering,
geometrical image transformations (resize, affine and perspective warping, generic tablebased
remapping), color space conversion, histograms, and so on.
 Video - a video analysis module that includes motion estimation, background subtraction, and
object tracking algorithms.
 calib3d - basic multiple-view geometry algorithms, single and stereo camera calibration, and
object pose estimation, stereo correspondence algorithms, and elements of 3D reconstruction.
 features2d - salient feature detectors, descriptors, and descriptor matchers.
Objdetect - detection of objects and instances of the predefined classes (for example, faces, eyes,
mugs, people, cars, and so on).
3.3 Setup Open CV library with msvs

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3.3.1 Download Open Cv from opencv.org
FIGURE: 24 downloading opencv
3.3.2Creating a new project
FIGURE: 25 Open New project in visual studio

23
FIGURE :26 Adding new property sheet in visual studio
FIGURE: 27 Edit Additional Include Libraries in Visual Studio
FIGURE: 28 Adding Additional Directives in Visual Studio

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3.4 Introduction to Pi B3 model:
The Raspberry Pi 3 Model B is the third generation Raspberry Pi. This powerful credit-card sized
single board computer can be used for many applications and supersedes the original Raspberry
Pi Model B+ and Raspberry Pi 2 Model B.
Whilst maintaining the popular board format the Raspberry Pi 3 Model B brings you a more
powerful processor, 10x faster than the first generation Raspberry Pi.
Additionally it adds wireless LAN & Bluetooth connectivity making it the ideal solution for
powerful connected designs.
Raspberry Pi 3 - Model B Technical Specification
● Broadcom BCM2387 chipset
● 1.2GHz Quad-Core ARM Cortex-A53
● 802.11 bgn Wireless LAN and Bluetooth 4.1 (Bluetooth Classic and LE)
● 1GB RAM
● 64 Bit CPU
● 4 x USB ports
● 4 pole Stereo output and Composite video port
● Full size HDMI
● 10/100 BaseT Ethernet socketbr
● CSI camera port for connecting the Raspberry Pi camera
● DSI display port for connecting the Raspberry Pi touch screen display
● Micro SD port for loading your operating system and storing data
● Micro USB power source
Raspberry Pi 3 - Model B Features
o Now 10x Faster - Broadcom BCM2387 ARM Cortex-A53 Quad Core Processor
powered Single Board Computer running at 1.2GHz!
o 1GB RAM so you can now run bigger and more powerful applications
o Fully HAT compatible
o 40pin extended GPIO to enhance your “real world” projects.
o Connect a Raspberry Pi camera and touch screen display (each sold separately)
o Stream and watch Hi-definition video output at 1080
o Micro SD slot for storing information and loading your operating systems.
o 10/100 BaseT Ethernet socket to quickly connect the Raspberry Pi to the Internet

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3.5 Setup of Pi B3 model:
FIGURE: 29 SETUP OF PI B3 MODEL
Step1:
Download the Raspberry Pi operating system
Step2:
Unzip the file that you just downloaded
Step3:
Download the Win32DiskImager software
Step4:
Format the SD card
Step5:
Writing Raspbian OS to the SD card
Step6:
Booting your Raspberry Pi for the first time. Connect HDMI to monitor. Connect power supply
and ETHERNET cable to Pi. Keyboard and mouse is also attached.
Step7:
On first boot you will come to the Raspi-configwindow.the Raspberry Pi will reboot and you will
see raspberrypi login: [Type:pi ]
You will be asked for your Password: [Type:raspberry ]
You will then see the prompt: pi@raspberry~ $
Hence you are ready for using it.

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3.6 Overall basic diagram of face detection system
The proposed system consists of both hardware units and software. A general block diagram of
the system is as shown below.
Block diagram of the proposed system
FIGURE: 30 BASIC BLOCK DIAGRAM OF FACE DETECTION SYSTEM
The Raspberry Pi is the heart of the system. We make use of a Model B Raspberry Pi which has a
size specification as 85.60 mm × 53.98 mm (3.370 in ×2.125 in), and around 15 mm deep. It has
a 512 MB built in RAM and operates at 700MHz.It has 2 USB ports and an Ethernet port.
Raspberry Pi Model B.
The system is programmed using Python programming language. We have developed three
algorithms, for face detection from a given image, from a folder of imagesand for real time face
detection.
A. Face detection from a given image
Histogram equalization is done on the input image.Haar classifier is used for image calculation
process and once face is detected, a red bounding box is drawn on the detected face. Detected face
and sub faces are saved and time taken for detection is printed.
B. Face detection from a folder of images
After Histogram equalization of the given image, Haar classifier is again used for image
calculation process. The difference from the first algorithm is that in addition to saving the detected
face to a specified folder, the algorithm also checks if each image belongs to the source directory.
If yes, the current file is named as a valid image with the file name. Otherwise, the file is named
as an invalid image.
C. Real time face detection
Video is captured real time using the webcam. As long as a face is detected, a red bounding box is
drawn and the video is displayed in the output window. The algorithm is efficient enough to detect
multiple faces.

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3.7 Face detection
Face detection is a computer technology that determines the locations and sizes of human faces in
arbitrary (digital) images. It detects facial features and ignores anything else, such as buildings,
trees and bodies. Face detection can be regarded as a more general case of face localization. In
face localization, the task is to find the locations and sizes of a known number of faces.
3.7.1 Haar Features:

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FIGURE: 31 HAAR CASCADE FEATURES
3.7.2 Face Detection Using Haar Cascade
Instead of applying all the 6000 features on a window, group the features into different stages of
classifiers and apply one-by-one. (Normally first few stages will contain very less number of
features). If a window fails the first stage, discard it. We don't consider remaining features on it. If
it passes, apply the second stage of features and continue the process. The window which passes
all stages is a face region.
FIGURE: 32 FACE DETECTION ON USING HAAR CASCADE

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3.7.3 Algorithm for Face Detection in opencv
1) Open CV already contains many pre-trained classifiers for face,eyes,smile etc. Those XML
files are stored in Open Cv/data/haarcascades/folder.
2) Load require XML classifiers
3) Convert image into gray scale
4) Use Cascade Classifier. detectMultiscale () to find faces and eyes from the image.
5) If faces and eyes are found, it will draw the circle on it.
3.7.4 Haar cascade with open CV on Pi
Here’s demonstrates to utilize the portability of the Raspberry Pi by developing a detection system
using the camera module extension is shown. The system should is capable of capturing a live
image, to detect any faces in the frame using a facial detection algorithm haar-cascade.
Figure a show remote monitoring for detecting the face using the Raspberry Pi with the Image
Sensor for processing the Haar Classifier algorithm on High Definition image. The image
processing tasks is been completed using the OpenCV library developed, which is compatible with
the Raspberry Pi board.
FIGURE: 33 OVERALL INTERFACING

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3.7.5 Output of Face Detection
FIGURE: 34 OUTPUT OF DETECTION
3.7.6 RESULT
The result of face detection is shown in above. Those are the frames extracted from the HD video
streaming. Sometimes, face detection algorithm may get more than one result even there is only
one face in the frame. In this case, a post image processing is been used for extracting the exact
face coordinates with OpenCV and SimpleCVHaar Classifier libraries. If the system output
provides more than one rectangle, which indicates the position of the face, the distance of centre
points of these rectangles has been calculated. If this distance is smaller than a pre-set threshold,
the average of these rectangles will be computed and set as the final position of the detected face.
we shift to OpenCV to evaluate the speed of this face tracking scheme, We found the face
detection is more suitable for real- time face detection using open cv since they requires less
CPU resource and costs shorter time.

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CHAPTER 4: SUMMARY
4.1 Summary of results:
Detection of face is done by image processing. Here we use Open Cv with programming language
C++. Initially camera will capture the image and face detection algorithm (i.e. Haar cascade) will
detect the face in image.
Then recognition algorithm is applied on this detected part.
4.2 Advantages:
No physical interaction is required
It is based on what you are. It uses your physical characteristic as face. In other biometrics solution
one has to intentionally interact. But in face recognition system camera do its work and also system
is secure.
Convenient and socially acceptable
One does not need to worry about to make communication. It is also compatible with existing
infrastructure. So it’s less expensive compared with other biometrics solution. It can install
anywhere and one can highly reliable upon it
More guaranteed of security of data
One can rely upon it that system is secure and can’t be easily stolen or cracked. It uses physical
access so data is secure without fear of stolen keys, cracking of password. Every person has unique
biometric trails so false acceptance will be low.
Availability of library
We are using Open Cvhere. It is free for academic as well as commercial use. And any new version
or any new arrival things about it is widely available.
Unbiased
Every person is treated equally.
Eco friendly
This system is not causing any harm to the environment as it is totally software based. It consumes
a small amount of energy to operate which can be bared instead of working day and night for the
same. And this small amount of energy do not causes any harm to the environment.

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4.3 Scope of future work
Face detection system can be used at any place where security is essential. With combination it
can be implemented at airport and railway stations, corporations, cash points, stadiums,
Government offices, businesses centres. For this database should be wide and highly accurate.
Some hardware part is also required like high pixel camera.
To increase the accuracy, various higher recognition algorithm can be use i.e MPCA, LDA(Linear
Discriminate Analysis) and for comparison different Distance algorithm can be used Manhattan
Distance and Mahalanobis distance.

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REFERENCES
[1] Jain, Anil K.; Ross, Arun, "Introduction to Biometrics" In Jain, AK; Flynn; Ross, A. Handbook
of Biometrics. Springer. pp. 1 –22, 2008.
[2]http://docs.opencv.org/doc/tutorials/introduction/windows_install/windows_install.html
[3] P.K. BISWAS. “Video Lecture Series Of Image Processing”, IIT Kharagpur
[4] Anton Obukhov, “Haar Classifiers for Object Detection with CUDA”, GPU Computing Gems.
Emerald Edition, 2011, pp.517-544.
[5] R. Lienhart, “An extended set of Haar-like features for rapid object detection”, Proceedings of
IEEE International Conference on Image Processing(ICIP'02), vol.1, pp.900-903, 2002.
[6] Open Computer Vision Library [Electronic resource], 2012, Mode of access:
http://sourceforge.net/projects/opencvlibrary. – Date of access: 25.06.2012.
[7] https://thepihut.com/products/raspberry-pi-3-model-b
[8] http://deliveryimages.acm.org/10.1145/970000/962102/figs/f1.jpg

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Appendix:
Copy of PPR:
Copy of PSAR:
CANVAS:

Face detection and recognition report

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