In this modern era, a huge revolution in technology is the introduction of biometric recognition system. One of the most useful biometric recognition system is fingerprint recognition system. The fingerprint recognition system is considered to most important biometric system in addition to other biometrics recognition systems

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FINGERPRINT SCANNER
A Multi Disciplinary Design project report submitted in partial fulfilment of the
requirements for the degree of
BACHELOR OF TECHNOLOGY
in
INFORMATION TECHNOLOGY
by
DHARMENDRA SINGH
(RA1511008030061)
And
VAIBHAV RAO
(RA1511008030072)
Under the guidance of
MS. DHOUMYA BHATT
Assistant Professor
Department of Information Technology
SRM Institute of Science & Technology
At
DEPARTMENT OF INFORMATION TECHNOLOGY
Modinagar, Ghaziabad - 201204
(April 2018)

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SRM INSTITUTE OF SCIENCE & TECHNOLOGY, NCR CAMPUS
DEPARTMENT OF INFORMATION TECHNOLOGY
Register No:
BONAFIDE CERTIFICATE
Certified to be the bonafide record of the work done by Dharmendra Singh &
Vaibhav Rao of B.Tech-IT, Third year, VI Semester for the award of B.Tech degree
course in the Department of Information Technology in Multi Disciplinary Design
Project during the Academic year-2017-18.
PROJECT IN-CHARGE HEAD OF DEPARTMENT
RA1511008030061 &
RA1511008030072

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ACKNOWLEDGEMENT
It is our privilege to express our sincerest regards to our project coordinator, MS. Dhoumya Bhatt for her
valuable inputs, able guidance, encouragement, whole-hearted cooperation and constructive criticism
throughout the duration of our project.
The team deeply express our sincere thanks to our Head of Department Dr. Anand Pandey for encouraging
and allowing us to present the project on the topic “Fingerprint Scanner “at our department premises for
the partial fulfillment of the requirements leading to the award of B-Tech degree.
The team are thankful to and fortunate enough to get constant encouragement, support and guidance from
all teaching staff of IT Department that helped us in successfully completing our project work.
The team heartily thank our friends for their help and suggestions during this project work.
Dharmendra Singh
Name of the Student 1
Vaibhav Rao
Name of the Student 2

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LIST OF CONTENTS
Sno. Chapter Page No.
1. Abstract 5
2. Chapter 1: Introduction 6
3. Chapter 2: Literature Survey 7
4. Chapter 3: Implementation 22
5. Chapter 4: Findings & Conclusion 29
6. Chapter 5: References & Research Papers 36

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ABSTRACT
In this modern era, a huge revolution in technology is the introduction of biometric recognition
system. One of the most useful biometric recognition system is fingerprint recognition system.
The fingerprint recognition system is considered to most important biometric system in addition
to other biometrics recognition systems. Fingerprint identification is popular because of the
inherent ease in acquisition, the numerous sources (ten fingers) available for collection, and their
established use and collections by law enforcement and immigration. The team has put an effort
in building a system which works on the principle of fingerprint recognition. By using this
system, fingerprint details will be stored in the system which will then allow the system to
recognize the same fingerprint whenever it is again used. Nobody else could use the system other
than the person whose fingerprint is in the system. This recognition system has two main parts
fingerprint verification and fingerprint identification Fingerprint verification refers to
authenticity of a person by his fingerprint. The user provides fingerprint together with identity
information. In the verification process template is retrieved based on the identification provided
and matching is performed.
Fingerprint identification is to specify one person’s identity by his fingerprints based upon the
unspecified conditions. In the identification of fingerprint, the process matches fingerprints with
the fingerprint database
for similarity.

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CHAPTER 1: INTRODUCTION
Fingerprint Identification is the method of identification using the impressions made by the
minute ridge formations or patterns found on the fingertips. No two persons have exactly the
same arrangement of ridge patterns, and the patterns of any one individual remain unchanged
throughout life. Fingerprints offer an infalible means of personal identification. Other personal
characteristics may change, but fingerprints do not.
Fingerprints can be recorded on a standard fingerprint card or can be recorded digitally and
transmitted electronically to the FBI for comparison. By comparing fingerprints at the scene of a
crime with the fingerprint record of suspected persons, officials can establish absolute proof of
the presence of identity of a person.
The practice of using fingerprints as a method of identifying individuals has been in use since the
late nineteenth century when Sir Francis Galton defined some of the points or characteristics
from which fingerprints can be identified. These “Galton Points” are the foundation for the
science of fingerprint identification, which has expanded and transitioned over the past century.
Fingerprint identification began its transition to automation in the late 1960s along with the
emergence of computing technologies. With the advent of computers, a subset of the Galton
Points, referred to as minutiae, has been utilized to develop automated fingerprint technology.

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CHAPTER 2: LITERATURE SURVEY
A fingerprint usually appears as a series of dark lines that represent the high, peaking portion of
the friction ridge skin, while the valleys between these ridges appears as white space
and are the low, shallow portion of the friction ridge skin. Fingerprint identification is based
primarily on the minutiae, or the location and direction of the ridge endings and bifurcations
(splits) along a ridge path. The images below present examples of fingerprint features: (a) two
types of minutiae and (b) examples of other detailed characteristics sometimes used during the
automatic classification and minutiae extraction processes. The types of information that can be
collected from a fingerprint’s friction ridge impression include the flow of the
friction ridges (Level 1 Detail), the presence or absence of features along the individual friction
ridge paths and their sequence (Level 2 Detail), and the intricate detail of a single
ridge (Level 3 Detail). Recognition is usually based on the first and second levels of detail or just
the latter. AFIS technology exploits some of these fingerprint features. Friction ridges do not
always flow continuously throughout a pattern and often result in specific characteristics such as
ending ridges, dividing ridges and dots, or other information. An AFIS is designed to interpret
the flow of the overall ridges to assign a fingerprint classification and then extract the minutiae
detail – a subset of the total amount of information available yet enough information to
effectively search a large repository of fingerprints.
Figure 2.1: Minutiae Figure 2.2: Other Fingerprint Characteristics

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Fingerprint Scanner
Fingerprint Scanners is a fingerprint recognition device’s for computer security equipped with
the fingerprint recognition module featuring with its superior performance, accuracy, durability
based on unique fingerprint biometric technology. Fingerprint Reader / Scanner is very safe and
convenient device for security instead of password, that is vulnerable to fraud and is hard to
remember. Use USB Fingerprint Scanner / Reader with our Biometrics software for
authentication, identification and verification functions that let your fingerprints act like digital
passwords that cannot be lost, forgotten or stolen.
There are four types of fingerprint scanners: the optical scanner, the capacitance scanner, the
‘ultrasonic scanner’ and the thermal scanners. The basic function of these three types of
scanners is to get an image of a person’s fingerprint and find a match for this print in the
database. The capacitance scanner is better, because the images are more exact and precise.
Scanners are used for scanning.
1. Optical scanners take a visual image of the fingerprint using a digital camera.

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Optical fingerprint scanners are the oldest method of capturing and comparingfingerprints. As the name
suggests, this technique relies on capturing an optical image, essentiallya photograph, and usingalgorithms
to detect unique patterns on the surface,such as ridges or uniquemarks, byanalyzing the lightest and darkest
areas of theimage.
Just like smartphone cameras,thesesensors can have a finite resolution, andthehigherthe resolution, the
finer details the sensor can discern about your finger, increasingthelevel of security. However,these sensors
capture much higher contrast images than a regular camera. These scanners typicallyhave a veryhigh
number ofdiodes per inch to capture these details up close. Of course,it’s verydark when your finger is
placed over the scanner, so optical scanners alsoincorporate arrays of LEDs as a flash to light up the picture
come scan time. Such adesignis a bit bulkyfor a smartphone though, where slim form factors are important.
Themajor drawback with optical scanners is that theyaren’t difficult to fool. As the technologyis only
capturing a2D picture,prosthetics and even other pictures of good enough qualitycanbe used to fool this
particular design. This type of scanners reallyisn’t secure enough to trust yourmost sensitive details to. It’s
also slowlybeingphased out these days.
Much like the earlydays of the resistivetouchscreen, you won’t find optical scanners used in anything but the
most cost effective pieces of hardwarethese days. Withincreasingdemand fortoughersecurity, smartphones
have unanimouslyadopted superior capacitivescanners, andthe falling cost oftechnologyhas made
capacitive alternatives viable for mid-range products too.
A fingerprint scanner system has two basic jobs -- it needs to get an image of your finger, and it
needs to determine whether the pattern of ridges and valleys in this image matches the pattern of
ridges and valleys in pre-scanned images.
There are a number of different ways to get an image of somebody's finger. The most common
methods today are optical scanning and capacitance scanning. Both types come up with the
same sort of image, but they go about it in completely different ways.

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2. Capacitive or CMOS scanners use capacitors and thus electrical current to form an
image of the fingerprint.
Themost commonlyfound type of fingerprint scanner usedtodayis the capacitive scanner. You’ll findthese
type of scanner inside various flagships, includingthe GalaxyS8, HTC U11, LG G6, andothers. Again the
name gives awaythe core component, providing you’re familiar with a little electronics,the capacitor.
Instead of creatinga traditional image of a fingerprint, capacitive fingerprint scanners use arrays tinycapacitor
circuits to collect data about a fingerprint. As capacitors can store electrical charge, connectingthem up to
conductive plates on the surface of the scanner allows them to be used to trackthedetails of a fingerprint. The
charge stored in the capacitor will be changedslightlywhen a finger’s ridge is placed overthe conductive
plates, while an air gap will leavethe charge at the capacitor relativelyunchanged. An op-amp integrator
circuit is usedtotrack these changes, which canthen be recorded byan analogue-to-digital converter.
Once captured, this digital data can be analysedtolook for distinctive and unique fingerprint attributes, which
can besaved for a comparison at a laterdate.What is particularlysmart about this design is that it is much
tougher to fool than anoptical scanner. The results can’t be replicated with animage andis incrediblytough
to fool with some sort ofprosthetic, as different materials will record slightlydifferent changes in charge at
the capacitor. Theonlyreal securityrisks come from eitherhardware or software hacking.

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3. Ultrasound fingerprint scanners use high frequency sound waves to penetrate the
epidermal (outer) layer of the skin.
Thelatest fingerprint scanningtechnologyto enter the smartphone space is anultrasonic sensor, which was
first announcedtobe inside the Le Max Prosmartphone. Qualcomm andits Sense ID technologyare also a
major part of the design in this particular phone.
To actuallycapturethedetails of a fingerprint, the hardware consists of both an ultrasonictransmitter and a
receiver. An ultrasonic pulse is transmitted against the finger that is placed overthe scanner.Some of this
pulse is absorbed and some of it is bounced back to the sensor, depending upon the ridges, pores andother
details that are uniqueto each fingerprint.

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4. Thermal scanners sense the temperature differences on the contact surface, in between
fingerprint ridges and valleys.
Thermal sensors use the same pyro-electric material that is used in infrared cameras. When a
finger is presented to the sensor, the fingerprint ridges make contact with the sensor surface and
the contact temperature is measured, the valleys do not make contact and are not measured. A
fingerprint image is created by the skin-temperature ridges and the ambient temperature measure
for valleys.
The biggest drawback of this technique is that the temperature change is dynamic and it only
takes about a tenth of a second for the sensor surface touching ridges and valleys to come to the
same temperature, erasing the fingerprint image. Additionally, this technology has many of the
same contamination and wear issues as other sensors. While it can operation over a wide range

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of temperatures, if the ambient temperature is close to the finger surface temperature the sensor
requires heating to create a temperature difference of at least 1 degree Centigrade.
Fingerprint processing includes two parts: fingerprint enrollment and fingerprint matching (the
matching can be 1:1 or 1: N). When enrolling, user needs to enter the finger two times. The
system will process the two-time finger images, generate a template of the finger based on
processing results and store the template. When matching, user enters the finger through optical
sensor and system will generate a template of the finger and compare it with templates of the
finger library. For 1:1 matching, system will compare the live finger with specific template
designated in the Module; for 1: N matching, or searching, system will search the whole finger
library for the matching finger. In both circumstances, system will return the matching result,
success or failure.
Atmega8 Controller
In 1996, AVR Microcontroller was produced by the “Atmel Corporation”. The
Microcontroller includes the Harvard architecture that works rapidly with the RISC. The features
of this Microcontroller include different features compared with other like sleep modes-6, inbuilt
ADC (analog to digital converter), internal oscillator and serial data communication, performs the
instructions in a single execution cycle. These Microcontroller were very fast and they utilize low
power to work in different power saving modes. There are different configurations of AVR
microcontrollers are available to perform various operations like 8-bit, 16-bit, and 32-bit. Please
refer the below link for; Types of AVR Microcontroller
Atmega8 Microcontroller

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AVR microcontrollers are available in three different categories such as TinyAVR, MegaAVR,
and XmegaAVR
 The Tiny AVR microcontroller is very small in size and used in many simple applications
 Mega AVR microcontroller is very famous due to a large number of integrated components,
good memory, and used in modern to multiple applications
 The Xmega AVR microcontroller is applied in difficult applications, which require high speed
and huge program memory.
Atmega8 Microcontroller Pin Description
The main feature of Atmega8 Microcontroller is that, all the pins of the Microcontroller support
two signals except 5-pins. The Atmega8 microcontroller consists of 28 pins where pins
9,10,14,15,16,17,18,19 are used for port B, Pins 23,24,25,26,27,28 and 1 are used for port C and
pins 2,3,4,5,6,11,12 are used for port D.
Atmega8 Microcontroller

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Pin Configuration
 Pin -1 is the RST (Reset) pin and applying a low level signal for a time longer than the minimum
pulse length will produce a RESET.
 Pin-2 and pin-3 are used in USART for serial communication
 Pin-4 and pin-5 are used as an external interrupt. One of them will activate when an interrupt
flag bit of the status register is set and the other will activate as long as the intrude condition
succeeds.
 Pin-9 & pin-10 are used as a timer counters oscillators as well as an external oscillator where
the crystal is associated directly with the two pins. Pin-10 is used for low-frequency crystal
oscillator or crystal oscillator. If the internal adjusted RC oscillator is used as the CLK source
& the asynchronous timer is allowed, these pins can be utilized as a timer oscillator pin.
 Pin-19 is used as a Master CLK o/p, slave CLK i/p for the SPI-channel.
 Pin-18 is used as Master CLK i/p, slave CLK o/p.
 Pin-17 is used as Master data o/p, slave data i/p for the SPI-channel. It is used as an i/p when
empowered by a slave & is bidirectional when allowed by the master. This pin can also be
utilized as an o/p compare with match o/p, which helps as an external o/p for the timer/counter.
 Pin-16 is used as a slave choice i/p. It can also be used as a timer or counter1 comparatively by
arranging the PB2-pin as an o/p.
 Pin-15 can be used as an external o/p of the timer or counter compare match A.
 Pin-23 to Pins28 have used for ADC (digital value of analog input) channels. Pin-27 can also
be used as a serial interface CLK & pin-28 can be used as a serial interface data
 Pin-12 and pin-13 are used as an Analog Comparator i/ps.
 Pin-6 and pin-11 are used as timer/counter sources.

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Atmega8 AVR Microcontroller Architecture
The Atmega AVR Microcontroller architecture includes the following blocks.
Architecture of Atmega8 Microcontroller
Memory: It has 1Kbyte Internal SRAM, 8 Kb of Flash program memory and 512 Bytes of
EEPROM.
I/O Ports: It has three ports, namely port-B, port-C and port-D and 23 I/O line can be attained
from these ports.
Interrupts: The two Exterior Interrupt sources are located at port D. Nineteen dissimilar interrupts
vectors supporting nineteen events produced by interior peripherals.
Timer/Counter: There are 3-Internal Timers are accessible, 8 bit-2, 16 bit-1, presenting numerous
operating modes & supporting internal/external clocking.

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Serial Peripheral Interface (SPI): ATmega8 microcontroller holds three integrated
communication devices. One of them is an SPI, 4-pins are allocated to the Microcontroller to
implement this system of communication.
USART: USART is one of the most powerful communication solutions.
Microcontroller ATmega8 supports both synchronous & asynchronous data transmission schemes.
It has three pins allocated for that. In many communication projects, USART module is widely
used for communication with PC-Microcontroller.
Two Wire Interface (TWI): TWI is an another communication device which is present in
ATmega8 microcontroller. It permits designers to set up a communication b/n two devices using
two wires along with a mutual GND connection, As the o/p of the TWI is made by means of open
collector o/ps, therefore external pull-up resistors are compulsory to make the circuit.
Analog Comparator: This module is incorporated in the integrated circuit that offers contrast
facility between two voltages linked to the two inputs of the comparator through External pins
associated with the Microcontroller.
ADC: Inbuilt ADC (analog to digital converter) can alter an analog i/p signal into digital data of
the 10-bit resolution. For a maximum of the low-end application, this much resolution is sufficient.
Atmega8 Microcontroller Applications
The Atmega8 microcontroller is used to build various electrical and electronic projects. Some of
the AVR atmega8 Microcontroller projects are listed below.
Some other Atmega8 based Projects
 AVR Microcontroller based LED Matrix Interfacing
 UART communication between Arduino Uno and ATmega8
 Interfacing of Opt coupler with ATmega8 Microcontroller
 AVR Microcontroller based Fire Alarm System
 Measurement of Light Intensity using AVR Microcontroller and LDR
 AVR Microcontroller based 100mA Ammeter
 ATmega8 Microcontroller based Anti-Theft Alarm System
 AVR Microcontroller based Interfacing of Joystick

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 AVR Microcontroller based Interfacing of Flex Sensor
 Stepper Motor Control using AVR Microcontroller
BC548
BC548 is a NPN transistor so the collector and emitter will be left open (Reverse biased) when
the base pin is held at ground and will be closed (Forward biased) when a signal is provided to
base pin. BC548 has a gain value of 110 to 800, this value determines the amplification capacity
of the transistor. The maximum amount of current that could flow through the Collector pin is
500mA, hence we cannot connect loads that consume more than 500mA using this transistor. To
bias a transistor we have to supply current to base pin, this current (IB) should be limited to 5mA.
When this transistor is fully biased, it can allow a maximum of 500mA to flow across the collector
and emitter. This stage is called Saturation Region and the typical voltage allowed across the
Collector-Emitter (VCE) or Base-Emitter (VBE) could be 200 and 900 mV respectively. When base
current is removed the transistor becomes fully off, this stage is called as the Cut-off Region and
the Base Emitter voltage could be around 660 mV.

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BC548 - NPN Transistor
BC548 Transistor Pinout
BC548 Pin Configuration
Pin Number Pin Name Description
1 Collector Current flows in through collector
2 Base Controls the biasing of transistor
3 Emitter Current Drains out through emitter

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BC548 Transistor Features
 Bi-Polar NPN Transistor
 DC Current Gain (hFE) is 800 maximum
 Continuous Collector current (IC) is 500mA
 Emitter Base Voltage (VBE) is 5V
 Base Current(IB) is 5mA maximum
 Available in To-92 Package
LCD(LM16ll)
The most commonly used LCDs found in the market today are 1 Line, 2 Line or 4 Line LCDs
which have only 1 controller and support at most of 80 charachers, whereas LCDs supporting
more than 80 characters make use of 2 HD44780 controllers.
Most LCDs with 1 controller has 14 Pins and LCDs with 2 controller has 16 Pins (two pins are
extra in both for back-light LED connections). Pin description is shown in the table below.
Fig 1: Character LCD type HD44780 Pin diagram
Pin No. Name Description
Pin no. 1 VSS Power supply (GND)
Pin no. 2 VCC Power supply (+5V)

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Pin no. 3 VEE Contrast adjust
Pin no. 4 RS
0 = Instruction input
1 = Data input
Pin no. 5 R/W
0 = Write to LCD Module
1 = Read from LCD module
Pin no. 6 EN Enable signal
Pin no. 7 D0 Data bus line 0 (LSB)
Pin no. 8 D1 Data bus line 1
Pin no. 9 D2 Data bus line 2
Pin no. 10 D3 Data bus line 3
Pin no. 11 D4 Data bus line 4
Pin no. 12 D5 Data bus line 5
Pin no. 13 D6 Data bus line 6
Pin no. 14 D7 Data bus line 7 (MSB)

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CHAPTER 3: IMPLEMENTATION
To make a fingerprint scanner work, few steps have to be followed:
1. Power on state
2. Display "Place Finger"
3. Poll for Keys and Finger Search
4. Display "Entry Successful" and Relay on for delay
1. "Time out"
2. "Process Failed"
5. Add Finger
1. Display "Add Finger" "Place Finger"
2. "Time out"
3. "Process Failed"
4. "Entry Successful" "ID=??"
6. Delete Finger
1. Display "Select ID"
2. Use UP/Down Keys
3. OK key to delete
4. "ID=?? Deleted"
The Screenshots of the code is given in the next page:

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Screenshots
Figure 3.1 & Figure 3.2

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Figure 3.3 & Figure 3.4

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Figure 3.5 & Figure 3.6

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Figure 3.7 & Figure 3.8

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Figure 3.9 & Figure 3.10

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Figure 3.11
This is the initial state of the simulation where all the connections are made between the LCD,
Microcontroller, Fingerprint scanner, switches, Transistor, Resistors. After all the connections are
made the code is run on this circuit.
Figure 3.12- Initial State

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CHAPTER 4: FINDINGS AND CONCLUSIONS
Output
When the code is executed, the program asks the user to place the finger on the scanner by
displaying the message “Add Finger” at the LED so that the fingerprint could be recognized
for further use.
Figure 4.2 – Add Finger

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This is the state when the code has been executed and the scanner recognizes the fingerprint
of the user.
Figure 4.3 - Access State

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When the Fingerprint used is not the one which has been scanned by the system, message
comes up “Finger Not Found”.
Figure 4.4 – Finger not found

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Conclusion
The code was successfully executed and the output was verified on the simulation by running the
code through the circuit made. The Fingerprint scanner was working properly and all the
commands were being properly executed.
Advantages
Access and Timekeeping
Most fingerprint scanning systems verify a person's identity to ensure they have permission to
access a secure area. Many employers also use fingerprint scanning systems to confirm when an
employee arrives or leaves work. Since time theft can cost the company a large amount of
money, using a fingerprint security system to track employee attendance can prevent another
coworker from clocking someone in or out. This results in more accurate time logs and fewer
mistakes.
Reliability
Fingerprint scanning systems provide a reliable way to track employees and you don't need to
worry about storing extra data, since the system only requires a fingerprint. With a fingerprint-
based system, employees don't need to worry about keeping cards or passwords safe.
Fingerprint-based systems provide the ability to detect an individual out of millions of
fingerprints accurately.
Security
Most other security systems have a higher risk of breaches caused by employee error. Someone
can take advantage of a badge carelessly left behind to access a forbidden area, or a skilled
worker may be locked out of his work area if he left his work badge at home. Fingerprint-based
systems provide additional security, since criminals can't easily fake a fingerprint, fingerprints
can't get misplaced and employees can't forget to bring their fingerprint to work. The hackers are
experts at finding out passwords to your computer , They likely don’t have a useable copy of
your fingerprint , Fingerprint Scanners present extra security , The human fingerprint contains
unique whorls and ridges that would be difficult for the average crook to duplicate .

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Equipment
Fingerprint-based systems can save money on hardware and material costs. Fingerprint scanning
systems tend to consist of a simple fingerprint reader and software that identifies the individual.
Most upgrades to the system come in the form of software-based upgrades, which reduces costs
further. With fingerprint systems, you don't have to worry about reprogramming badges,
assigning employee passcodes or maintaining inventory.
High Accuracy
Fingerprint Scanner offers very high accuracy , It is the most economical biometric PC user
authentication technique , It is one of the most developed biometrics and small storage space
required for the biometric template that reduces the size of the database memory required .
Faster Work Completion
Fingerprint speeds up the secure transaction or the registration process compared to the complex
series of verification of emails, passwords, encrypted data and more, The fingerprints are used
with a combination of other tools such as iris cameras or voice recognition.
Faking is nearly Impossible
Fingerprint scanners are difficult to fake than the identity cards, you can’t guess the fingerprint
pattern like you can guess the password, Fingerprint scanners will continue to be the most widely
used security method (they will be used hand-in-hand with classic passwords).
Small and easy to use
Fingerprint scanners are circular and flat. They are very convenient with a swipe or press of your
finger.

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Future Scope
Moving the fingerprint sensor under the glass is a major design change and will, therefore,
require a next-generation technology that is expected to evolve in three distinct phases.
Phase 1 involves locating the sensor under the glass in the bezel/ink area, and will leverage
existing capacitive sensing technology. Capacitive sensing is currently the dominant technology
by far, as there are very few smartphones with other fingerprint scanning technology on the
market as of this writing.
Capacitive sensing works by discerning minute changes in an electric field, and that requires
obtaining a sufficient signal-to-noise ratio (SNR). The signal in this case is the detection of the
many tiny “ridges” and “valleys” in the fingerprint. The electrical noise comes from the display
itself, and is quite “loud” by comparison.
Achieving a sufficient SNR requires the finger to be relatively close to the sensor, and the state-
of-the-art today is a distance of approximately 0.3mm or 300 microns. Because cover glass is
thicker than this, it is necessary to shave or thin the topside or underside of the glass where the
sensor is mounted.
Another possible technology involves acoustic scanning in the ultrasound range. Ultrasonic
scanning can be made quite sensitive, but the high power consumption and/or high cost will
likely make this technology viable only for certain high-end devices. Optical technology is
another possibility in the bezel area, but will require the ability to sense through the various
colors of the zone.
Phase 2 will involve moving the sensor to a fixed location within the display area. This will
preclude the ability to thin the glass as that would cause the image to become distorted. The
extremely low SNR with capacitive sensing for glass thickness above 0.3mm will, therefore,
render this technology impractical. With the sensor now able to function through clear glass,
optical technology becomes increasingly attractive and even preferable because the photo diode
sensors will be able to take advantage of the display itself as the requisite light source. Acoustic

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technology will also remain a viable option in this phase, provided the power consumption and
cost can be made competitive with optical.
Phase 3 presents the most difficult challenge: enabling fingerprint scanning anywhere within the
display. Just as capacitive sensing has become the technology of choice in Phase 1, the optical
technology used in Phase 2 will likely become the preferred alternative in this final phase. For
this reason, engineers can be expected to consider the challenges involved in whole-display
scanning during Phase 2. Previous advances in touch/display integration will also have an
influence in both of these latter two phases.
The industry is shifting away from cumbersome passwords to more secure biometrics, and for
very good reasons. From hackers hacking, to users forgetting them, to juggling and constantly
changing dozens of them, passwords are archaic and change is paramount. Fingerprint
authentication is leading the way, with innovation around every corner. Next steps will include
adding multi-factor biometrics which will once again greatly enhance security and further
improve usability.

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CHAPTER 5: REFERENCES AND RESEARCH PAPERS
1. Qijun Zhao et al. [6] proposed an adaptive pore model for fingerprint pore extraction. Sweat
pores have been recently employed for automated fingerprint recognition, in which the
pores are usually extracted by using a computationally expensive skeletonization method or
a unitary scale isotropic pore model. In this paper, however, author shows that real pores are
not always isotropic. To accurately and robustly extract pores, they propose an adaptive
anisotropic pore model, whose parameters are adjusted adaptively according to the
fingerprint ridge direction and period. The fingerprint image is partitioned into blocks and a
local pore model is determined for each block. With the local pore model, a matched filter is
used to extract the pores within each block. Experiments on a high resolution (1200dpi)
fingerprint dataset are performed and the results demonstrate that the proposed pore model
and pore extraction method can locate pores more accurately and robustly in comparison
with other state-of- the-art pore extractors.
2. Moheb R. et al. [7] proposed an approach to image extraction and accurate skin detection
from web pages. This paper proposes a system to extract images from web pages and then
detect the skin color regions of these images. As part of the proposed system, using
BandObject control, they build a Tool bar named “Filter Tool Bar (FTB)” by modifying the
Pavel Zolnikov implementation. In the proposed system, they introduce three new methods
for extracting images from the web pages (after loading the web page by using the proposed
FTB, before loading the web page physically from the local host, and before loading the web
page from any server). These methods overcome the drawback of the regular expressions
method for extracting images suggested by Ilan Assayag. The second part of the proposed
system is concerned with the detection of the skin color regions of the extracted images. So,
they studied two famous skin color detection techniques. The first technique is based on the
RGB color space and the second technique is based on YUV and YIQ color spaces. They
modified the second technique to overcome the failure of detecting complex image‟s
background by using the saturation parameter to obtain an accurate skin detection results.
The performance evaluation of the efficiency of the proposed system in extracting images
before and after loading the web page from local host or any server in terms of the number

37. 37
of extracted images is presented. Finally, the results of comparing the two skin detection
techniques in terms of the number of pixels detected are presented.
3. Manvjeet Kaur et al. [8] proposed a fingerprint verification system using minutiae extraction
technique. Most fingerprint recognition techniques are based on minutiae matching and have
been well studied. However, this technology still suffers from problems associated with the
handling of poor quality impressions. One problem besetting fingerprint matching is
distortion. Distortion changes both geometric position and orientation, and leads to
difficulties in establishing a match among multiple impressions acquired from the same
finger tip. Marking all the minutiae accurately as well as rejecting false minutiae is another
issue still under research. Our work has combined many methods to build a minutia extractor
and a minutia matcher. The combination of multiple methods comes from a wide
investigation into research papers. Also some novel changes like segmentation using
morphological operations, improved thinning, false minutiae removal methods, minutia
marking with special considering the triple branch counting,
4. Hoi Le et al. [9] proposed online fingerprint identification with a fast and distortion tolerant
hashing method. National ID card, electronic commerce, and access to computer networks
are some scenarios where reliable identification is a must. Existing authentication systems
relying on knowledge-based approaches like passwords or token-based such as magnetic
cards and passports contain serious security risks due to the vulnerability to engineering-
social attacks and the easiness of sharing or compromising passwords and PINs. Biometrics
such as fingerprint, face, eye retina, and voice offer a more reliable means for authentication.
However, due to large biometric database and complicated biometric measures, it is difficult
to design both an accurate and fast biometric recognition. Particularly, fast fingerprint
indexing is one of the most challenging problems faced in fingerprint authentication system.
In this paper, they present a specific contribution by introducing a new robust indexing
scheme that is able not only to fasten the fingerprint recognition process but also improve
the accuracy of the system.
5. Ratha et al. [30] proposed an adaptive flow orientation based segmentation or binarization
algorithm. In this approach the orientation field is computed to obtain the ridge directions at
each point in the image. To segment the ridges, a 16x16 window oriented along the ridge
direction is considered around each pixel. The projection sum along the ridge direction is

38. 38
computed. The centers of the ridges appear as peak points in the projection. The ridge
skeleton thus obtained is smoothened by morphological operation. Finally minutiae are
detected by locating end points and bifurcations in the thinned binary image.
6. Anil Jain et al. [10] proposed a Pores and Ridges: Fingerprint Matching Using Level 3
Features. Fingerprint friction ridge details are generally described in a hierarchical order at
three levels, namely, Level 1 (pattern), Level 2 (minutiae points) and Level 3 (pores and
ridge shape). Although high resolution sensors (∼1000dpi) have become commercially
available and have made it possible to reliably extract Level 3 features, most Automated
Fingerprint Identification Systems (AFIS) employ only Level 1 and Level 2 features. As a
result, increasing the scan resolution does not provide any matching performance
improvement [17]. They develop a matcher that utilizes Level 3 features, including pores
and ridge contours, for 1000dpi fingerprint matching. Level 3 features are automatically
extracted using wavelet transform and Gabor filters and are locally matched using the ICP
algorithm. Our experiments on a median-sized database show that Level 3 features carry
significant discriminatory information. EER values are reduced (relatively ∼20%) when
Level 3 features are employed in combination with Level 1 and 2 features.
7. Mayank Vatsa et al. [11] proposed an combining pores and ridges with minutiae for improved
fingerprint verification. This paper presents a fast fingerprint verification algorithm using
level-2 minutiae and level-3 pore and ridge features. The proposed algorithm uses a two-stage
process to register fingerprint images. In the first stage, Taylor series based image
transformation is used to perform coarse registration, while in the second stage, thin plate
spline transformation is used for fine registration. A fast feature extraction algorithm is
proposed using the Mumford–Shah functional curve evolution to efficiently segment contours
and extracts the intricate level-3 pore and ridge features. Further, Delaunay triangulation based
fusion algorithm is proposed to combine level-2 and level-3 information that provides
structural stability and robustness to small changes caused due to extraneous noise or non-
linear deformation during image capture. They defines eight quantitative measures using
level-2 and level-3 topological characteristics to form a feature super vector. A 2n-support
vector machine performs the final classification of genuine or impostor cases using the feature
super vectors. Experimental results and statistical evaluation show that the feature super vector

39. 39
yields discriminatory information and higher accuracy compared to existing recognition and
fusion algorithms.
8. Umut Uludaga et al. [14] proposed a Biometric template selection and update: a case study
in fingerprints. Sweat pores have been recently employed for automated fingerprint
recognition, in which the pores are usually extracted by using a computationally expensive
skeletonization method or a unitary scale isotropic pore model. In this paper, however, real
pores are not always isotropic. To accurately and robustly extract pores, they propose an
adaptive anisotropic pore model, whose parameters are adjusted adaptively according to the
fingerprint ridge direction and period. The fingerprint image is partitioned into blocks and a
local pore model is determined for each block. With the local pore model, a matched filter is
used to extract the pores within each block. Experiments on a high resolution (1200dpi)
fingerprint dataset are performed and the results demonstrate that the proposed pore model
and pore extraction method can locate pores more accurately and robustly in comparison with
other state-of-the-art pore extractors.
9. Coetzee and Botha [31] proposed a binarization technique based on the use of edges
extracted using Marr-Hilderith operator. The resulting edge image is used in conjunction
with the original gray scale image to obtain the binarized image. This is based on the
recursive approach of line following and line thinning. Two adaptive windows, the edge
window and the gray-scale window are used in each step of the recursive process. To begin
with, the pixel with the lowest gray-scale value is chosen and a window is centered on it.
The boundary of the window is then examined to determine the next position of the window.
The window is successively position to trace the ridge boundary and the recursive process
terminates when all the ridge pixels have been followed to their respective ends.
10. Ruud M. Bolle et al. [33] proposed the evaluation techniques for biometrics-based
authentication systems (FRR). Biometrics-based authentication is becoming popular because
of increasing ease-of-use and reliability. Performance evaluation of such systems is an
important issue. They endeavor to address two aspects of performance evaluation that have
been conventionally neglected. First, the “difficulty” of the data that is used in a study
influences the evaluation results. They propose some measures to characterize the data set so
that the performance of a given system on different data sets can be compared. Second,
conventional studies often have reported the false reject and false accept rates in the form of

40. 40
match score distributions. However, no confidence intervals are computed for these
distributions, hence no indication of the significance of the estimates is given. In this paper,
they compare the parametric and nonparametric (bootstrap) methods for measuring confidence
intervals. They give special attention to false reject rate estimates.
11. Wang Yuan et al. [36] proposed a real time fingerprint recognition system based on novel
fingerprint matching strategy. In this paper they present a real time fingerprint recognition
system based on a novel fingerprint minutiae matching algorithm. The system is developed
to be applicable to today's embedded systems for fingerprint authentication, in which small
area sensors are employed. The system is comprised of fingerprint enhancement and quality
control, fingerprint feature extraction, fingerprint matching using a novel matching
algorithm, and connection with other identification system. Here they describe their way to
design a more reliable and fast fingerprint recognition system which is based on today's
embedded systems in which small area fingerprint sensors are used. Experiment on FVC
database show our system has a better performance than compared. And for the image
enhancement and matching techniques they use high efficiency, it can also give a real time
identification result with high reliability.
12. Wei Cui et al. [37] proposed the research of edge detection algorithm for fingerprint images.
This paper introduces some edge detection operators and compares their characteristics and
performances. At last the experiment show that each algorithm has its advantages and
disadvantages, and the suitable algorithm should be selected according the characteristic of
the images detected, so that it can perform perfectly. The Canny Operator is not susceptible
to the noise interference; it can detect the real weak edge. The advantage is that it uses two
different thresholds to detect the strong edge and the weak edge, and the weak edge will be
include in the output image only when the weak edge is connected to the strong edge. The
Sobel Operator has a good performance on the images with gray gradient and high noise, but
the location of edges is not very accurate, the edges of the image have more than one pixel.
The Binary Image Edge Detection Algorithm is simple, but it can detect the edge of the image
accurately, and the processed images are not need to be thinned, it particularly adapts to
process various binary images with no noise. So each algorithm has its advantages and
disadvantages, and the suitable algorithm should be selected according to the characters of the
images been detected, then it can performance perfectly.

41. 41
13. Shunshan li et al. [38] proposed the Image Enhancement Method for Fingerprint Recognition
System. In this paper fingerprint image enhancement method, a refined Gabor filter, is
presented. This enhancement method can connect the ridge breaks, ensures the maximal gray
values located at the ridge center and has the ability to compensate for the nonlinear
deformations. it includes ridge orientation estimation, a Gabor filter processing and a refined
Gabor filter processing. The first Gabor filter reduces the noise, provides more accurate
distance between the two ridges for the next filter and gets a rough ridge orientation map while
the refined Gabor filter with the adjustment parameters significantly enhances the ridge,
connects the ridge breaks and ensures the maximal gray values of the image being located at
the ridge center. In addition, the algorithm has the ability to compensate for the nonlinear
deformations. Furthermore, this method does not result in any spurious ridge structure, which
avoids undesired side effects for the subsequent processing and provides a reliable fingerprint
image processing for Fingerprint Recognition System. In a word, a refined Gabor filter is
applied in fingerprint image processing, then a good quality fingerprint image is achieved, and
the performance of Fingerprint Recognition System has been improved.
14. S. Mil'shtein et al. [39] proposed a fingerprint recognition algorithm for partial and full
fingerprints. In this study, they propose two new algorithms. The first algorithm, called the
Spaced Frequency Transformation Algorithm (SFTA), is based on taking the Fast Fourier
Transform of the images. The second algorithm, called the Line Scan Algorithm (LSA), was
developed to compare partial fingerprints and reduce the time taken to compare full
fingerprints. A combination of SFTA and LSA provides a very efficient recognition technique.
The most notable advantages of these algorithms are the high accuracy in the case of partial
fingerprints. At this time, the major drawback of developed algorithms is lack of pre-
classification of examined fingers. Thus, they use minutiae classification scheme to reduce the
reference base for given tested finger. When the reference base had shrunk, they apply the
LSA and SFTA.
15. Another paper proposed a novel approaches for minutiae filtering in fingerprint images.
Existing structural approaches for minutiae filtering use heuristics and adhoc rules to eliminate
such false positives, where as gray level approach is based on using raw pixel values and a
super-vised classifier such as neural networks. They proposed two new techniques for
minutiae verification based on non-trivial gray level features. The proposed features

42. 42
intuitively represent the structural properties of the minutiae neighborhood leading to better
classification. They use directionally selective steerable wedge filters to differentiate between
minutiae and non-minutiae neighborhoods with reasonable accuracy. They also propose a
second technique based on Gabor expansions that result in even better discrimination. They
present an objective evaluation of both the algorithms. Apart from minutiae verification, the
feature description can also be used for minutiae detection and minutiae quality assessment.
16. Deepak Kumar Karna et al. [41] proposed normalized cross-correlation based fingerprint
matching. To perform fingerprint matching based on the number of corresponding minutia
pairings, has been in use for quite some time. But this technique is not very efficient for
recognizing the low quality fingerprints. To overcome this problem, some researchers suggest
the correlation technique which provides better result. Use of correlation-based methods is
increasing day-by-day in the field of biometrics as it provides better results. In this paper, they
propose normalized cross-correlation technique for fingerprint matching to minimize error
rate as well as reduce the computational effort than the minutiae matching method. The EER
(Equal Error Rate) obtained from result till now with minutiae matching method is 3%, while
that obtained for the method proposed in this paper is approx 2% for all types of fingerprints
in combined form.
17. Asker M. Bazen et al. [42] proposed a correlation-based fingerprint verification system. In
this paper, a correlation-based fingerprint verification system is presented. Unlike the
traditional minutiae-based systems, this system directly uses the richer gray-scale information
of the fingerprints. The correlation-based fingerprint verification system first selects
appropriate templates in the primary fingerprint, uses template matching to locate them in the
secondary print, and compares the template positions of both fingerprints. Unlike minutiae-
based systems, the correlation-based fingerprint verification system is capable of dealing with
bad-quality images from which no minutiae can be extracted reliably and with fingerprints
that suffer from non-uniform shape distortions. Experiments have shown that the performance
of this system at the moment is comparable to the performance of many other fingerprint
verification systems.
18. David G. Lowe [43] proposed an approach to distinctive image features from scale-invariant
keypoints. This paper presents a method for extracting distinctive invariant features from
images that can be used to perform reliable matching between different views of an object or

43. 43
scene. The features are invariant to image scale and rotation, and are shown to provide robust
matching across a substantial range of affine distortion, change in 3D viewpoint, addition of
noise, and change in illumination. The features are highly distinctive, in the sense that a single
feature can be correctly matched with high probability against a large database of features
from many images. This paper also describes an approach to using these features for object
recognition. The recognition proceeds by matching individual features to a database of
features from known objects using a fast nearest-neighbor algorithm, followed by a Hough
transformation to identify clusters belonging to a single object, and finally performing
verification through least-squares solution for consistent pores parameters. This approach to
recognition can robustly identify objects among clutter and occlusion while achieving near
real-time performance.
CONCLUSION
This chapter presents the work done by other researcher related to fingerprint verification
system, pores extraction and matching system. In this chapter description about all reference
papers are summarized. This paper presents a brief survey of fingerprint level 3 features
extraction and matching approach which is a novel approach, its characteristics, design
issues and applications. It also describes an overview of Level 1 and level 2 features, in the
literature and their functionalities. Along with that, it has through discussion of SIFT
algorithm. Finally, it presents an novel approach for level 3 feature extraction and matching
algorithm. Since the technology is going to its zenith, the way of its journey is not smooth
and many loopholes are there.
The proposed work is an attempt to overcome some weakness regarding security concern of
the system. The experimental results demonstrate that the proposed approach and its
associated pore extraction method can detect pores more accurately and robustly, and can
help to improve the verification accuracy of pore based fingerprint recognition systems.
There are many method exists to make it unextractable by adversaries, like one is to use
multi-biometric traits under a single process, but it need extra sensors setup for each kind of
traits, the proposed technique gives an extra edge to such systems which use single type of
sensor and give more security.

44. 44
This approach will give the technology new amplitude in order to provide a secure way of
authentication, in which the pores are logically extracted. The proposed algorithm performs
better than existing recognition algorithms and fusion algorithms. Along with other
advantages, in all biometric systems fingerprint based systems are more efficient than other
multimodal system, so it minimizes FAR and FRR.
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