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CHAPTER 1: INTRODUCTION AND LIERATURE REVIEW
1.1. Introduction to Smart Aadhaar Identification System
The Unique Identification Authority of India (UIDAI) is a central government agency
of India.Its objective is to collect the biometric and demographic data of residents, store
them in a centralized database, and issue a 12-digit unique identity number
called Aadhaar to each resident.[5][6] It is considered the world's largest national
identification number project.
This technology can be used in various expect because of its features. We are using its
unique card’s QR code feature to authorize the identity of a person in a system and system
can be used anywhere like, from hotel management system to various big organizations. Our
System not only decode aadhaar QR code but it will verify it with the huge Aadhaar database
to validate the identity of the user.

Figure 1.1.1: AADHAAR IDENTIFICATION SYSTEM

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1.2. LITERATURE REVIEW
Aadhaar Card [1]
Aadhaar card is a number that serves as a proof of
identity and address, anywhere in India. Any
individual, irrespective of age and gender, who is a
resident in India and satisfies the verification
process laid down by the UIDAI, can enrol for
Aadhaar.
This Adhaar card also comprise of a QR code
which comprise the basic information of a person
such as UID , name ,address ,etc.
Aadhaar project has been linked to some public
subsidy and unemployment benefit schemes like
the domestic LPG scheme and MGNREGS. In
these Direct Benefit Transfer schemes, the subsidy
money is directly transferred to a bank account
which is Aadhaar-linked.
On 29 July 2011, the Ministry of Petroleum and
Natural Gas signed a memorandum of
understanding with UIDAI. The Ministry had
hoped the ID system would help them eliminate
loss of the subsidized kerosene and LPG. In May
FIGURE 1.2.1: AADHAAR CARD
2012, the government announced that it will begin
issuing Aadhaar-linked MGNREGS cards. On 26
November 2012, a pilot project was launched in 51 district.

Under the original policy for liquefied petroleum gas subsidies, the customers bought gas
cylinders from retailers at subsidized prices, and the government compensated companies
for their losses. Under the current Direct Benefit Transfer of LPG (DBTL), introduced in
2013, customers had to buy at the full price, and the subsidy would be then directly credited
to their Aadhaar-linked bank accounts.

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Computer Vision[2]
Computer vision is an interdisciplinary field that deals with how computers can be made
to gain high-level understanding from digital images or videos. From the perspective
of engineering, it seeks to automate tasks that the human visual system can do.
Computer vision tasks include methods for acquiring, processing, analyzing and
understanding digital images, and in general, deal with the extraction of high-dimensional
data from the real world in order to produce numerical or symbolic information, e.g., in the
forms of decisions. Understanding in this context means the transformation of visual images
(the input of the retina) into descriptions of the world that can interface with other thought
processes and elicit appropriate action. This image understanding can be seen as the
disentangling of symbolic information from
image data using models constructed with
the aid of geometry, physics, statistics, and
learning theory.

FIGURE 1.2.2: COMPUTER VISION

As a scientific discipline, computer vision is concerned with the theory behind artificial
systems that extract information from images. The image data can take many forms, such as
video sequences, views from multiple cameras, or multi-dimensional data from a medical
scanner. As a technological discipline, computer vision seeks to apply its theories and
models for the construction of computer vision systems.
Sub-domains of computer vision include scene reconstruction, event detection, video
tracking, object recognition, object pose estimation, learning, indexing, motion estimation,
and image restoration.

APPLICATION OF COMPUTER VISION
Applications range from tasks such as industrial machine vision systems which, say, inspect
bottles speeding by on a production line, to research into artificial intelligence and
computers or robots that can comprehend the world around them. The computer vision and
machine vision fields have significant overlap. Computer vision covers the core technology
of automated image analysis which is used in many fields. Machine vision usually refers to
a process of combining automated image analysis with other methods and technologies to
provide automated inspection and robot guidance in industrial applications. In many
computer vision applications, the computers are pre-programmed to solve a particular task,
but methods based on learning are now becoming increasingly common. Examples of
applications of computer vision include systems for:



Controlling processes, e.g., an industrial robot;
Navigation, e.g., by an autonomous vehicle or mobile robot;
3|Page







Detecting events, e.g., for visual surveillance or people counting;
Organizing information, e.g., for indexing databases of images and image sequences;
Modeling objects or environments, e.g., medical image analysis or topographical
modeling;
Interaction, e.g., as the input to a device for computer-human interaction, and
Automatic inspection, e.g., in manufacturing applications.

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CHAPTER 2: METHODOLOGY ADOPTED
2.1. Flow Diagram

FIGURE 2.1.1: FLOW DIAGRAM OF PROCESS ADOPTED

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2.2. Procedure of QR code detection[3]
2.2.1. Receiving Live feed from picamera
Raspberry pi comes with a camera csi port which can be used to attach HD cameras.
Raspberry pi has various libraries inbuilt which are used to get input from the input devices
like camera, pen drives etc. Hence this input from camera is used as input to feed our
python program. Where further detection process can continued.

FIGURE 2.2.1: LIVE FEED TO PI CAMERA

2.2.2. Detection and identification of qr code
The first task is to identify reliable patterns in the QR Code.
Detection comprise of detecting where qr code is in the picture taken by the camera. It
extracts the qr code frame and detects its contour. For this purpose it is necessary to
determine all the contour in the frame taken and find the one which satisfy the required
conditions.

FIGURE 2.2.2: VARIOUS PARTS OF QR CODE

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2.2.3. Qr code orientation and decoding
Orientation is very important for qr code detection. Because if the orientation is not correct
then decoding can’t be done. But we can’t expect user to orient its Aadhaar card properly
every time, its tedious for user and hence will be not user friendly. To solve this problem
our program must be able to detect orientation by itself and it can be done by different
approaches, like:
After detection of the three markers in the QR code we determine the relation between the
three markers. Using these relation we can determine which one is the top, right and bottom
one. One the position of the markers is determined we can reorganize the orientation of the
QR-code.

FIGURE 2.2.3: MARKERS OF QR CODE

FIGURE 2.2.4: ORIENTAION TRIANGLE

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2.2.4. Qr code data extraction
Decoding qr code provides a string which encoded into the qr code, it can be an xml string
like in case of aadhaar card or any other string like Wi-Fi passwords etc. But the main point
is that we have to extract required information from the decoded string.
It can be done using the programming language we are working on like in our case it is
Python. We can write suitable program to search and store required information in database
or as required by the programmer.

2.2.5. Checking for authorization of aadhaar card
As our system is a aadhaar identification system, we have our pre-built database of
authorized aadhaar card user and when someone show its card in the picamera, his aadhaar
information like uid, and birth date and name is verified from the database and only if the
data is verified user is authorized otherwise system will alert the admin about the
unauthorized person try to use system.

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CHAPTER 3: DESIGNING OF PROJECT
3.1 Hardware Used:
3.1.1. Raspberry pi[4][5]
One line answer to the about question would be, “Pi is a single-board computer”. Pi is a small
scale computer in the size little bigger than a credit card, it packs enough power to run games,
word processor like open office, image editor like Gimp and any program of similar magnitude.
The Raspberry Pi is a series of credit card-sized single-board computers developed in
the United Kingdom by the Raspberry Pi Foundation to promote the teaching of basic computer
science in schools and developing countries.
Pi is based on a Broadcom SoC (System of Chip) with an ARM processor [~700 MHz], a GPU and
256 to 2 GB RAM. The boot media is an SD card [which is not included], and the SD card can
also be used for persist data. Now that you know that the RAM and processing power are not
nearly close to the power house machines you might have at home, these Pi’s can be used as a
Cheap computer for some basic functions, especially for experiments and education. The Pi
comes in three Configurations and we will discuss the specifications of those in the coming
sections. The cost of a Pi is around $35 for a B Model and is available through many online and
physical stores..

[

FIGURE 3.1.1: RASPBERRY PI BOARD

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