BIOMETRIC TECHNOLOGY

1. SEMINAR ON
BIOMETRIC SECURITY
NIDHI NAYAN
DEPARTMENT OF ECE
RGD NO-1301294235

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4. • The term is derived from greek words “BIO” means Life
and “METRON” means Measurement.
• “ The technology used for identification of a user based
on a physical or behavioral characteristic, such as a
fingerprint, iris, face, voice or handwriting is called
Biometrics”.
• The authentication system authenticates your identity
and verifies if you are the person you say you are. It,
according to the result, allows or denies access to the
system.

5. Types of Biometric-
1.Behavioral
-voice
-signature
-DNA
2. Physiological
-fingerprint
-palmprint
-face
-hand geometry
-iris

6. • In modern approach, Biometric characteristics can be
divided in two main classes:
• Physiological are related to the shape of the body and
thus it varies from person to person Fingerprints, Face
recognition, hand geometry and iris recognition are
some examples of this type of Biometric.
• Behavioural are related to the behavior of a person.
Some examples in this case are signature, keystroke
dynamics and of voice . Sometimes voice is also
considered to be a physiological biometric as it varies
from person to person.

7. • The eye contains a certain
structure surrounding the
pupil which is known as
iris.
• Iris varies from person to
person and two irises can
never be identical.
• Even the left and right eye irises are different from
• each other. Here the user is asked to stand couple
of inches of inches away from the iris scanner,
keeping eye in a certain position.
• Iris Scan is a bigger and expensive system, but is
very accurate.

8. Hand geometry is basically the
analysis and measuring of
both hands and fingers. The
user is required to place its
hand at the hand-scanner with
the fingers in proper position
as they were at the time of
sign-up. A 32 thousand pixel
digital camera is used to
measure the thickness, length
and width of the hands and
fingers. There are many
different types of hand devices
available in the market.

10. The voices of people vary from one to another. This
fact was used when devising voice scan technology.
The voice of a user is recorded using a microphone
reading device. And the system is able to recognize
the voice later because of the special characteristics
of human voice. It is important that the
circumstances at the time of recording and
authenticating are same. Things like background
noise and equipment’s quality might influence the
system. The best part of using this technique is that
it requires no expensive equipment.

11. To be very accurate. And by using this technique
less people will object to this type of authentication.
This technique uses a special pen and tablet to
capture the way user signs its name. As much as the
signature is important there are other features that
are considered important.
It measures when a user’s
put the pen on the tablet
and when he removes it,
the pressure on the table,the
speed by which he signs,etc.

12. • Connect an apparatus to the computer and it scans the finger
of a person and authenticates identity of an individual. If it
authenticates the person, it will give the person the access to
enter the system. And if it doesn’t authenticate, you are not
given access to the system. Fingerprints vary from person to
person.
• The have some special characteristics that make them
different from the finger print of other person. The
fingerprints of a person remain unchanged throughout life,
except in case of some injury or damage.
• Fingerprint scanning can be done in a number of ways. There
are some systems that scan the distinct patterns on finger.
• Fingerprint systems should be cleaned regularly as dust may
affect the reading and the system may not be able to
authenticate the user.

13. • Human fingerprints have been discovered on a large
number of archaeological artifacts and historical items
• In 1684, the English plant morphologist, Nehemiah
Grew, published the first scientific paper reporting his
systematic study on the ridge, furrow, and pore structure
• In 1788, a detailed description of the anatomical
formations of fingerprints was made by Mayer.
• In 1823, Purkinji proposed the first fingerprint
classification, which classified into nine categories
• In 1975, The FBI funded the development of fingerprint
scanners

14. Fingerprint Formation

15. • classification is necessary to reduce the search time and
computational complexity.
• The FBI database has 70 million fingerprints

16. BLOCK DIAGRAM

17. • Optical
• Silicon Based Capacitive Sensors
• Ultrasound
• Thermal

18.  The first step is to obtain a clear image of the fingerprint.
 Enhancement is carried out so as to improve the clarity of ridge
and furrow structures of input fingerprint images based on the
estimated local ridge orientation and frequency.
 For grayscale images, areas lighter than a particular threshold
are discarded, and those darker are made black.
Original Enhanced

19. • Minutiae localization is the next step.
•Even a very precise image has distortions and false minutiae that
need to be filtered out.
•Anomalies caused by scars, sweat, or dirt appear as false
minutiae, and algorithms locate any points or patterns that don't
make sense, such as a spur on an island or a ridge crossing
perpendicular to 2-3 others (probably a scar or dirt).
•A large percentage of would-be minutiae are discarded in this
process.

20. The point at which a ridge ends, and the point where a
bifurcation begins, are the most rudimentary minutiae.
Once the point has been situated, its location is
commonly indicated by the distance from the core, with
the core serving as the 0,0 on an X,Y-axis. In addition to
the placement of the minutia, the angle of the minutia is
normally used. When a ridge ends, its direction at the
point of termination establishes the angle. This angle is
taken from a horizontal line extending rightward from
the core, and can be up to 359.

21. •The matching accuracy of a biometrics-based authentication system relies on the
stability of the biometric data associated with an individual over time.
•The biometric data acquired from an individual is susceptible to changes introduced
due to improper interaction with the sensor (e.g., partial fingerprints), modifications
in sensor characteristics (e.g., optical vs. solid-state fingerprint sensor), variations in
environmental factors (e.g.,dry weather resulting in faint fingerprints) and temporary
alterations in the biometric trait itself (e.g., cuts/scars on fingerprints).
•Thus, it is possible for the stored template data to be significantly different from those
obtained during authentication, resulting in an inferior performance (higher false
rejects) of the biometric system.
Variation in fingerprint exhibiting partial
overlap.

22. •Multiple templates, that best represent the variability
associated with a user's biometric data, should be stored in
the database. (E.g. One could store multiple impressions
pertaining to different portions of a user's fingerprint in
order to deal with the problem of partially overlapping
fingerprints.)
• There is a tradeoff between the number of templates, and
the storage and computational overheads introduced by
multiple templates.
•For an efficient functioning of a biometric system, this
selection of templates should be done automatically.

23. •Automatic Minutiae Detection: Minutiae are essentially terminations and
bifurcations of the ridge lines that constitute a fingerprint pattern.
•Automatic minutiae detection is an extremely critical process, especially in low-
quality fingerprints where noise and contrast deficiency can originate pixel
configurations similar to minutiae or hide real minutiae.
Algorithm:
•The basic idea here is to compare the minutiae on
the two images.
•The figure alongside is the input given to the system,
as can be seen from the figure the various details of
this image can be easily detected. Hence, we are in a
position to apply the AMD algorithm.

24. Algorithm (contd.)
• The next step in the algorithm is to
mark all the minutiae points on the
duplicate image of the input fingerprint
with the lines much clear after feature
extraction.
• Then this image is superimposed
onto the input image with marked
minutiae points as shown in the figure.
• Finally a comparison is made with
the images in the database and a
probabilistic result is given.

25. • Each fingerprint impression produces an image of 768 x
768 ( when digitized at 500 dpi)
• In AFIS applications, this needs more amount of
memory space to store these images
• Neither lossless methods or JPEG compression
techniques are satisfactory
• A new compression technique called Wavelet Scalar
Quantization (WSQ) is introduced to compress the
images

26. • Banking Security - ATM security,card transaction
• Identification of Criminals
• Voting
• Physical Access Control (e.g. Airport)
• National ID Systems
• Passport control (INSPASS)
• Prisoner, prison visitors, inmate control
• Identification of missing children
• Secure E-Commerce (Still under research) Information System
Security

27. 3-D fingerprint
• A new generation of touchless live scan devices that
generate a 3D representation of fingerprints is
appearing.
• Several images of the finger are acquired from different
views using a multicamera system, and a contact-free
3D representation of the fingerprint is constructed.
• This new sensing technology overcomes some of the
problems that intrinsically appear in contact-based
sensors such as improper finger placement, skin
deformation, sensor noise or dirt.

28. • https://en.wikipedia.org
• www.slideshare.net
• “Overview of Biometrics & Fingerprint Technology” - Dr. Y.S.
Moon
• www.techtarget.com
• https://www.quora.com