power point presentation

2. Contents
1. Definition.
2. Introduction.
3. Features.
4. Uses.
5. Advantages.
6. Applications.
7. Fingerprint
Recognition Gadgets.
8. Types of fingerprint.
9. Characteristics of
Fingerprint.
10. Variations of
Fingerprint.
11. Fingerprinting
Sensors.
12. Algorithms.
13. Different Techniques
of Fingerprinting.
14. Requirements for
Fingerprint Processor.
15. Block Diagram of
Fingerprint
Processing System.
16 . Fingerprint
Mechanism using DIP.
17. Steps in Fingerprint
Processing.
18. Conclusion.
19. References.

3. Definition
A fingerprint in its narrow
sense is an impression left by
the friction ridges of a
human finger.

4. Introduction
i. Fingerprint Identification/Recognition is one of the most well-
known & publicized biometrics.
ii. Because of their uniqueness & consistency over time,
fingerprints have been used for Identification for over a century
& recently becoming automated.
iii. The concept of fingerprint came in 19Th century by Sir Francis
Galton, which were known as Galton Points.
iv. In 1969, it was a major push by FBI (Federal Bureau of
Investigation) & CIA (Central Intelligence Agency) officials in
United States to develop a completely “Automated Fingerprint
Identification Process” to track Criminals, Smugglers & keep
their records as an Identification to track them in American
Airports & Ports.
v. Hence, making this attempt famous & overwhelming around the
world.

5. Features
 Unique - The ridges and their
characteristics of our fingers are
unique. Each person has distinct
and unique ridges on finger, sole,
and palm.

6. Uses
 Personal Identification:-The patterns of fingerprint are
extensively modified but the quantity, characteristics,
and position of the ridges remained unchanged.
Eg:- Fingerprint in an ID-Card.
 Criminal Cases:- During investigations on criminal
cases - Fingerprinting is the most affordable and at the
same time the best way to identity a criminal. The
presence of fingerprints in the crime scene is the only
way that will prove who was the criminal. This is
because a fingerprint alone is enough conclusive
evidence.

7. Advantages &
Applications
 Advantages
 To prevent stealing of
identity - The fingerprints
are important to be
included in some
important documents
such as passports, social
security cards, bank
accounts, driving
licenses, and others to
avoid the use or access
of unauthorized persons.
 Applications
Fingerprints are important
in manufacturing
biometrics-based electronic
gadgets such as –
 Finger printing systems.
 Security systems door.
 Locking door access
control systems.
 Attendance fingerprint
systems.
 Digital fingerprints of
security systems.

8. Fingerprint Recognition
Gadgets

9. Types of fingerprint
 Visible prints :- It is also called patent prints. It can be
seen when blood, dirt, ink or grease on the finger come
into contact with a smooth surface and leave a friction
ridge impression that is visible without development.
 Latent prints :- They are not apparent to the naked eye.
They can be made sufficiently visible by dusting, fuming
or chemical reagents.
 Impressed prints :- It also called plastic prints. They are
visible and can be viewed or photographed without
development.

10. Characteristics Variations

11. Fingerprinting
Sensors
A fingerprint sensor is
an electronic device used
to capture a digital
image of the fingerprint
pattern. The captured
image is called a live
scan. This live scan
is digitally processed to
create a biometric
template (a collection
of extracted features)
which is stored and used
for matching. This is an
overview of some of the
more commonly used
fingerprint
sensor technologies. :-
 Optical:- Optical fingerprint imaging involves
capturing a digital image of the print using visible
light. This type of sensor is, in essence, a
specialized digital camera.
 Ultrasonic:- Ultrasonic sensors make use of the
principles of medical ultra-sonography in order to
create visual images of the fingerprint. Unlike
optical imaging, ultrasonic sensors use very high
frequency sound waves to penetrate the
epidermal layer of skin.
 Capacitance:- Capacitance sensors utilize the
principles associated with capacitance in order to
form fingerprint images.
 Passive capacitance:- A passive capacitance
sensor uses the principle outlined above to form
an image of the fingerprint patterns on the dermal
layer of skin.
 Active capacitance:- Active capacitance
sensors use a charging cycle to apply a voltage to
the skin before measurement takes place.

12. Algorithms
Matching algorithms are used to compare previously stored
templates of fingerprints against candidate fingerprints
for authentication purposes. In order to do this either the
original image must be directly compared with the candidate
image or certain features must be compare.
 Pattern-based (or image-based) algorithms:-Pattern
based algorithms compare the basic fingerprint patterns
(arch, whorl, and loop) between a previously stored template
and a candidate fingerprint. This requires that the images be
aligned in the same orientation. The candidate fingerprint
image is graphically compared with the template to determine
the degree to which they match.

13. Different Techniques of
Fingerprinting Traditional Technique:-
The traditional or most common technique of fingerprinting is taking impressions of a
person's hand with the help of ink. Pressing fingers covered in ink, on to a paper is the way
of obtaining fingerprints.
 Digital Scanning Technique:-
A sensitive touch-pad is used to capture the fingerprints of a person or a suspect in this
method. The impression of fingerprint recorded on the touch-pad is then compared with
thousands of impressions with the help of software .
 Lifting Technique:-
In this technique, oil from hands which are left behind are captured by means of powders
made from resinous polymers. This technique is specially used in Crime Investigations.
 Laser Technique:-
The laser technique is one of the most useful for capturing fingerprints. In this
fingerprinting technology, the fingerprints from many different surfaces can be lifted by
means of laser.
 Bullet Fingerprinting :-
This technique is considered as a sure shot method of fingerprint identification, because
even wiping and washing of the surface cannot remove the sweat gland deposits
completely. It is possible to visualize the fingerprint.

14. Requirements for Fingerprint
Processor
 Speed.
 Size of the fingerprint identification database
 Power consumption.
 The core architecture of a processor: A dual
multiply-accumulate (MAC) or single-MAC core
should be considered for this application.
 Bus architecture.
 Power management integration.
 Others: Other important considerations include
dedicated hardware for different addressing
modes, loop control and execution control and
peripheral integration.

15. Block Diagram of Fingerprint
Processing System.

16. Fingerprint Mechanism using DIP
 A popular and reliable way to compare fingerprints is to analyze the minutiae of the fingerprint. To do so,
a system must first capture the image of a fingerprint and process the image to make it easy for image
analysis. After the analysis, the minutiae will be extracted and saved in a template format. The system
must then store the data of the minutiae to be used for future comparison.
 Fingerprint sensors
Based on the fingerprint processing diagram, a sensor is the “front-end” of the system, playing an import
role: it captures the image of the fingerprint. The two types of popular sensors used in fingerprint
analysis are the optical and swipe sensors. Here are basic descriptions of how they operate.
 Optical sensor:-An optical fingerprint sensor captures a digital image of the fingerprint by using
visible light. The following describes the operation principle of an optical sensor.
 First, the light source inside of a sensor will emit light to illuminate the surface of the finger.
 The light reflected from the finger passes to a solid-state pixels sensor (either a charge-coupled device
[CCD] or complementary metal oxide semiconductor [CMOS] image sensor), which captures a visual
image of the fingerprint. On the path of the light, a specially designed lens will be used.
 Swipe sensor:-A swipe sensor is a type of active capacitance sensor. Here is how an active
capacitance sensor works.
 First, the active capacitance sensors will apply a voltage to the skin. This will generate an electrical field
in the space between the skin and sensor.
 Since the electrical field between the finger-skin and sensor follows the pattern of the ridges in the
dermal skin layer, the effective capacitance will be measured across this field.
 The distances between skin and sensors can then be calculated mathematically by using this
equation: C = ε0*εr*(A/d); here “d” represents the distance.
 Based on the calculated distances across the field, the fingerprint image can be mapped.

17. OPTICAL SENSOR. SWIPE SENSOR.

18. Continued…
 Fingerprint processing
After an image of a fingerprint is captured, a sequence of image processing
algorithms will be applied to the captured image. In fingerprint
authentication applications, two main types of technologies are being used:
one is called minutiae based, and the other is called image based.
 Minutiae are the special spots of a fingerprint that show the changing of the
print. These spots have been predefined and categorized. Two main
features of minutiae, which are extracted from these spots:
 Ridge ending.
 Bifurcation.
 However, the minutiae are not limited to these two features.
 In a minutiae-based system, the goal is to find the minutiae in the captured
fingerprint image and compare them with fingerprints that are in the
database. In order to extract the minutiae successfully, the fingerprint
images must be preprocessed, which usually involves computationally-
intensive image processing algorithms.

19. The digital image signal processing
steps include:
Segmentation and filtering.
Contrast enhancement.
Orientation calculation.
Gabor filtering.
Binarization.
Thinning.
Feature extraction.

20. Continued….
 Segmentation and filtering:- The main purpose of segmentation is to
get the “good” area of a captured fingerprint image, then separate this
valid fingerprint from the image background. Some filtering can be
applied to the image to filter out the noise in the image.
 Contrast enhancement:- After segmentation, the image is subjected
to gray stretch to increase the global contrast of the image. Because
the skin of an entire finger has a similar color, the more interesting
parts of the fingerprint and the less interesting areas have a very low
level of contrast.
Mathematically, this type of operation is transformation.
 Orientation calculation:- There are different implementations for
mapping the orientation of a fingerprint. The most popular algorithm to
map the orientation of fingerprints is the gradient-based approach.
The gradient ∇(x, y) at point [x,y] of I (an image) is a two-dimensional
vector [∇x(x, y)∇y(x,y)].
Mathmatically, gradient ∇ is the first derivative of the image, the ∇x and
∇y are the derivatives on X and Y directions, respectively.

21. Continued….
 In a fingerprint system, to numerically calculate the ∇x(x, y)and ∇y(x,y), a
popular method is to use the Sobel operator. The following is a 3x3 Sobel mask:
The gradient can be calculated as following:

22. Continued….
 Then the gradient’s direction, angle θ, can be calculated as:
These calculations will be applied across the fingerprint image, and an
orientation map will be created.
 Gabor filtering:- A Gabor filter is defined as a two-dimensional Gaussian
function multiplied by a sinusoidal plane wave function:

23. Continued….
 Here the xθ and yθ are the point coordinate [x,y] rotated (90-θ) degrees, defined
as:
 θ represents the orientation.
 f represents the frequency of the ridge-valley-ridge pattern; it can
be the reciprocal of the width of ridge-valley measurement.
 σx and σy are the standard deviations of the Gaussian envelope
on x and y directions, respectively.
 Binarization :- The goal of binarization is to convert the gray-
level image to binary level “1” or “0.” In other words, this space-
changing operation converts the image to black or white with no
levels in between.

24. Continued….
 Thinning:- After the binarization, the ridges and valleys are in black
and white, respectively, but the width of lines may be wider than
one pixel. To further reduce the complexity of minutiae extraction, a
thinning algorithm will be applied to the image.
 Feature extraction. Figure 5 illustrates the fingerprint image for
each processing step during the entire fingerprint analysis flow.
After these steps of signal processing, we will obtain the final
fingerprint image. The minutiae-based features, such as ridge
ending and bifurcation, will be found and extracted from the final
image.

25. Conclusion
 Our fingerprints are unique and
permanent from birth until death.
 Our Fingerprints Never Lie as Our
Faces Do.
 Only if we know the unique knowledge
to this science.
 This is because fingerprint has its own
language and it is hard to understand
the truth it reveals.

26. References
 www.fbi.gov
 www.biometrics.gov
 www.altavista.com
 www.fpvte.nist.gov
THANKYOU….!!!!