Biometrics – An Introduction and Current State
Divya J
M.E. (VLSI and Embedded system)
G. H. Raisoni College of Engineering & Management,
Wagholi, Pune
divyavikram73@gmail.com
Prof. Dr.R.S.Bichkar
G. H. Raisoni College of Engineering & Management,
Wagholi, Pune
rajankumar.bichkar@raisoni.net
The recent couple of decades had seen the
Abstract:
This paper describes the popularly used physical and
behavioral biometric techniques. These techniques
are mostly used for the purpose of identification and
recognition of users, which forms a significant part of
security systems. Authentication is of utmost
importance while dealing with security. This paper
provides a crisp review on the biometrics technologies
beginning with introduction to the concept of
biometrics and its comparison with other similar
techniques currently available. Next, we have a quick
glance through the various modalities in the
technology and finally a quick comparison on these
modalities along with the advantages and
disadvantages with respect to each of them.
availability of wide range of financial instruments
Keywords: Biometrics, Iris scan, Fingerprint recognition, Face
recognition, Retina scan, Hand geometry, Voice recognition.
identification in place. This resulted in the emergence
being brought to regular usage. Although this has
brought a tremendous improvement in which the
financial services have been perceived and made use of
by the masses, the occurrences of financial crimes has
grown
multiple
times.
This
implied
that
the
accompanying technologies to combat such frauds are
getting outdated and the time expects more powerful
technologies. These technologies must be capable of
getting more stringent variants of authentication and
of range of security devices that made heavy use of
1. Introduction
B
biometric concepts, since biometrics was the only
IOMETRIC technologies have become one of
the trendsetters in today’s world. With drastic
technical improvements being achieved in the recent
past, it is all set to change the way in which most of the
critical day-to-day tasks had been performed till date.
One of the major issues of concern in a wider society is
“Security”. As a result of this, authentication has
gained
significant
importance.
Researchers
had
constantly been intrigued by a question, “How can
machines be made to identify human beings?” It was a
breakthrough when it struck them that, unique
biological traits of the human beings can be harnessed,
for this to be achieved. With this idea as a baseline,
most security systems built over the principles of
biometrics can successfully identify individuals with
their unique biological traits.
known field to be capable of catering to such demands.
The biometric traits captured by such devices are
processed through a complex set of algorithms and
mathematical calculations to effectively produce the
desired output. Moreover, the fear of theft of the
captured data can be overcome by another layer of
security, provided by encryption, cryptography and
related concepts.
This paper is organized as follows: This section
one throws light on the background information
pertaining to the topic. In the next section two, we have
a history of biometric technologies, basic classification
and also provide performance metrics. Section three
gives a brief introduction to the biometric technologies,
along with the advantages and disadvantages that each
one of these technologies have to offer. Section four
takes a dip in the implementation details, by describing
 19th century France: Alphonse Bertillon, a member
the representation scheme and matching algorithm.
of police, invented Anthropometry that used body
Based on the factors for an effective biological trait, we
measurements to identify criminals.
then have a look at the comparison among the
 Late
19th
century
Argentina/UK/US:
An
biometric technologies in section five, which is later
Argentine police official was the first person to
used in section six to figure out the suitability of these
maintain fingerprint files. Sir Francis Galton
technologies for certain applications in particular.
discovered that fingerprints of two individuals
Finally, the last section hints on the scope available for
cannot be the same. In 1920s, the U.S. Military and
future research in this field.
FBI started using fingerprints for identification.
 Late 20th century: In the last quarter of the
Identification Techniques
Traditionally,
the
identification process can be
20thcentury,
biometrics
accomplished by any of the three techniques;
i. Something you possess: Refers to the token based
authentication systems using physical objects such
as ID cards. The main disadvantage of this
based
authentication
systems.
Certain
programs/tools are available that can crack the
passwords. Also, the person may forget a
password/PIN, leading to manual intervention.
iii. Something you are: The biometric systems make
use of this technique. It efficiently overcomes the
disadvantage of the above two techniques, since it
makes it nearly impossible to steal the identity.
based
identification
techniques
have
for
rapidly
developed. For example, in 1985 it was proposed
that irises were unique and first iris recognition
algorithm was patented in 1994.
The biometric technologies can be broadly
approach is that, it can be forged and misused.
ii. Something you know: Refers to password/PIN
computer-aided
classified based on physiological and/or behavioral
characteristics, which can be used for identification or
verification. The physiological biometrics includes
fingerprint, hand geometry, iris, face, retina, etc.
whereas the behavioral biometrics includes signature,
gait, voice, key stroke, etc.
At any given point of time, a biometric device is
said to operate in either of the two modes ‘Verification’
or ‘Identification’. The verification (or Authentication)
deals with one-to-one comparison of the captured
iv.
2. History,
classifications,
performance
criteria of biometrics
biometric with an available template. This template is
previously enrolled against the person’s record (known
individual). Similarly, the identification mode deals
The term ‘Biometrics’ is derived from two Greek
with
words, ‘Bios’ (life) and ‘Metrics’ (measure). The basic
biometric image is compared with multiple templates
idea of using a person’s bodily features like eyes, face
previously enrolled and available in the database.
and fingers for his identification was developed over
one-to-many
comparison,
where
captured
Biometric systems can be evaluated for their
time, with the efforts of many people across the globe.
performance on the basis of following criteria:
 14th century China: In order to distinguish their
 False Acceptance Rate (FAR) or False Match
children from one another, merchants were using
their palm and footprints.
Rate (FMR): It measures the percentage of invalid
input that is erroneously accepted. This also depends
Figure 1 shows how a fingerprint scanner records the
on the threshold value.
identities.
 False Rejection Rate (FRR) or False Non-Match
Rate (FNMR): It measures the percentage of valid
input that is erroneously rejected.
 Receiver/Relative
(ROC):
The
ROC
Operating
plot
Characteristic
provides
a
visual
characterization depicting a fair balance between the
FAR and FRR.
 Equal Error Rate (EER) or Crossover Error Rate
(CER): It depicts the rate at which rejection errors
and acceptance errors are equal. Generally lower
Figure1: Fingerprint Recognition
EER means more accuracy.
 Failure to Enroll Rate (FTE or FER): It is the rate
at which attempts for the creation of a template from
the input turns unsuccessful. It is usually a result of
low quality inputs.
 Failure to Capture Rate (FTC): In case of
automatic systems, this gives a probability that the
system fails to recognize a correctly presented
biometric input.
 Template Capacity: It provides the highest number
of data sets that can be stored in the system.
3.2 Face Recognition
Face Recognition is a process of verifying or
identifying an individual against a video frame or a
digital image from a video source, mostly using a
computer application. Typically this is accomplished
by comparing facial features from the captured image
against the template available in the database as shown
in figure 2. Unlike other technologies, face recognition
works automatically. Algorithms are developed mainly
based on two approaches i.e. either Geometric (feature
based) or Photometric (view based).In order that the
face recognition system provides the desired output the
3. Encapsulated Technologies
3.1 Fingerprint Recognition
acquired image must automatically detect whether a
face is present. If yes, locate the face. Finally, the
Fingerprint recognition refers to the automated method
located faces should be recognizable from general
of verifying a match between two or more human
perspective (from any pose).
fingerprint. The underlying fingerprint matching
techniques can be classified as either Minutiae based or
Correlation based.
In Minutiae based recognition, initially the scanner
tries to find the minutiae points on the finger and later
maps its relation with the template that is already saved
in database. Whereas, correlation based recognition
scheme needs a specific location for registration, which
may be impacted by image translation and rotation.
Figure 2: Face Recognition
3.3 Signature Recognition:
places his hand palm down on a metal surface with
This technique refers to the recognition of an
guidance pegs. These pegs are used to confirm that the
individual’s signature using devices. The dynamic
fingers are positioned correctly and also verify the
signature
correct hand position.
verification
technology
provides
the
capability to identify a computer client, by harnessing
the behavioral biometrics of a hand written signature as
shown in figure 3.Signatures are widely accepted as a
means of authentication, including for commercial and
legal transactions. However, studies on signatures have
proved that they tend to change as time passes and are
significantly influenced by the mental and physical
state of the signatories. Also, for a considerable amount
Figure 4: Hand Geometry Recognition
of people, their signatures vary to some extent. This
has been the case even when the signatures marked in a
3.5 Voice recognition:
successive manner were considered. Further, there
In voice recognition sound sensations of a person
exist some professional forgers, whose services could
is measured and compared to an existing dataset as
be availed to fool the system.
shown in figure 5. The person to be identified is
usually required to speak a secret code, which facilitate
the verification process. An individual’s voice depends
on the shape and size of mouth, vocal tracts, lips and
nasal cavities that are collectively referred to as
appendages. They are used in the synthesis of the
sound.
Figure 3: Signature Recognition
3.4 Hand geometry:
As the name implies, these biometric systems take into
account
the
various
measurements
from
the
individual’s hand. Some of these measurements include
shape & size of the palm, length & width of the fingers,
etc, which is shown in figure 4. It has 3D image of top
& sides of hand & fingers collected. From these
Figure 5: Speech Recognition
images, feature vectors are extracted and compared
3.6 Iris recognition
with the dataset feature vector. Even though the
This technique is based on the iris of the eye. Iris is a
recognition devices are bulky, the identification
colored area that surrounds pupil. The iris structure
process is performed quickly in seconds. The user
contains a highly unique complex pattern. The gray
scale image of the eye is obtained and presented to the
Schemes and Matching Algorithms are listed below
software, which then covers the iris by creating a net of
in Table 1:
curves. The Iris code is generated based on the
darkness of the points that emerges along the lines of
Modality
Representation
Scheme
Minutiae
Distribution
Matching
Algorithm
String Matching.
Face
Principal
Component
Analysis (PCA),
Local
Feature
Analysis(LFA)
Euclidean
Distance, Bunch
graph matching.
Hand
Geometry
Length/width
finger/Palm
Euclidean
Distance
Voice
Mel-Cepstum
Hidden
Markov
Model, Gaussian
Mixture Model
Iris
Texture Analysis,
Key-point
Extraction
Hamming
Distance
the software. Figure below shows iris recognition
Finger-
system.
print
of
Figure 6: Iris Recognition
3.7 Retinal scanning
This technique scans the complex network of blood
vessels in the retina, which is shown in the figure 7.
Table1: Representation scheme and matching algorithms
Since the capillaries that supplies blood to the retina is
a complex structure, even the identical twins do not
5. Comparison
share similar pattern. The scan is performed by
directing a beam of low energy light (infrared) into the
individual’s eye. This beam detects a standardized path
in the retina and discovers a unique pattern. This
pattern can then be converted to computer code and can
be used as a template for future use.
of
various
biometric
technologies
5.1 Comparison based on Performance:
Different biometric technologies discussed in Section 3
possess their own set of pros and cons, making it
suitable for different set of applications. These
technologies can be weighed on the basis of seven
factors described below, which can help to determine
the most specific areas of application domain.
i.
Universality: Measures how good is the system in
capturing each of the members of the population.
ii.
Distinctiveness: Measures how well is the system
able to distinguish among individuals.
iii. Permanence: Measures how well the system
remains resistant to biological changes throughout
Figure 7: Retina Recognition
4. Representation
algorithms:
scheme
Commonly
the enrolled person’s lifetime.
and
used
matching
Representation
iv. Collectability: Measures how good is the system
in acquiring measurements to capture the data.
v.
Performance: Measures how quickly, accurately
that meets all requirements. Table 3 below enlists the
and robustly the system collects data and responds.
strengths and weakness associated with each of the
vi. Acceptability: Measures to what extent are the
biometric technologies. It serves as a ready reference to
individuals in the relevant population willing to
understand
accept the system.
technologies.
the
applicability
of
each
of
these
applications
may
vii. Circumvention: Measures how difficult would it
be to imitate the data in order to manipulate the
6. Applications:
Biometric-based
system.
The comparison among the biometric technologies on
authentication
typically consist of one or more of the components:
workstation & network access, data protection,
the basis these factors is described in Table 2 below.
application logon, single sign-on, Web security,
5.2 Comparison
based
on
Strengths
and
weaknesses:
transaction security and remote access to resources.
The services promised by e-government and e-
Even though all biometric systems seem to work in
commerce can be achieved successfully only if strong
similar manner, the quality of template and the ease of
personal authentication procedures are being utilized.
enrolment are found to play an important role in overall
The benefits of these technologies have already been
success. Out of many Characteristics, each biometric
proved by the relative financial and non-financial fields
system has its own strengths and weaknesses. There are
such as secure e-banking, investments and other
no particular biometric systems, which can be
financial transactions, health and social service and
applicable
retail sales.
to
all
applications.
Based
on
the
application’s usage and the biometric characteristics
features, we are able to select a particular biometric
The biometrics technologies, we are talking about
is a key player in network authentication environment
Modality
Fingerprint
Face
Signature
Hand Geometry
Speaker
Recognition
Iris
Retina
Strength
▪ Highly accurate
▪ Very reliable
▪ Consistent
▪ Difficult to duplicate
▪ Readers are economical
▪ Systems require less space
▪ Quick identification
▪ No physical contact - Hygienic
▪ Mass identification possible
▪ Convenient for user
▪ Safe - No privacy concerns
▪ Does not require cooperation of the
subject
▪ Wide acceptance (Government, legal)
▪ Fast and simple training
▪ Economical
▪ Low storage requirements
▪ Inconsistent user detectable on
enrollment
▪ Convenient acquisition
▪ Good performance
▪ Works in harsh environments
▪ Simple & easy to use
▪ Economical
▪ Unaffected by individual anomalies
▪ Public acceptance
▪ No contact required
▪ Simplest to acquire
▪ Safe - No privacy concerns
▪ Does not require cooperation of the
subject
▪ Least obtrusive biometric measure
▪Contact less Process
▪More Secure
▪Highly Reliable.
Weakness
▪ Damageable
▪ Scanners can be tricked
▪ User Resistance (Ethical issues)
▪ Privacy concerns of criminal implications
▪ Enrolment issues (cold, wet, desquamation, elder)
▪ Require a large amount of computational resources
▪ Low Performance - lighting, disguise & image
resolution
▪ Low Accuracy - expression, viewpoint, etc.
▪ Reliability is slightly lower
▪ Cultural & Religious issues
▪ More expensive & complex
▪ Influenced by physical and emotional conditions
▪ Difficulty for Illiterates
▪ Error Rate is high
▪ Forgeable
▪ Require contact with the writing instrument
▪ Inconsistent (Change over time)
▪ Large size of hand geometry device is needed
▪ Not highly unique
▪ Variable during growth period of children
▪ Difficult to embed in other device
▪ Unsuitable for one-to-many applications
▪ Difficult to control sensor and channel variances
▪ Tiring effect (Ex: teachers, sales person)
▪ Susceptible to noise
▪ Error prone
▪ Distant microphone leads to more errors
▪ Difficult to control sensor and channel variances
▪ May covered by eyelids, eyelashes, reflections from
the cornea lens and changes in lighting
▪ Difficult for multiple users of different heights to use
in succession.
▪ Significantly more expensive.
▪ Person to be identified must hold the head still and
look into the camera.
▪ Modern cataract surgeries can change iris structure.
▪ Alcohol consumption causes deformation of iris
pattern.
▪ Scanners can be easily fooled by high quality image
of an iris.
▪ Low occurrence of false positives
▪ Very intrusive. Not user friendly.
▪ False negative rate is considerably low
▪ It has the stigma of consumer's thinking it is
▪ Speedy Result
potentially harmful to the eye.
▪ Highly reliable
▪ Very expensive.
▪ Accuracy affected by diseases such as cataract.
▪ Accuracy affected by severe astigmatism
▪ Not very user friendly
Table 3: Strengths and weakness associated with each of the biometric technologies
in large-scale enterprise. When utilized as standalone
system or integrated with other technologies such as
digital signatures, smart cards and encryption keys,
biometrics is highly anticipated to pervade onto each
and every aspects of the economy as well as our daily
lives.
7. Research Challenges:
In the preceding sections, we saw the extent to which
Biometric Technologies have grown, to provide
effective solutions to some of the most crucial day-today activities. However from the performance aspects,
there still exists a descent scope to be explored. A
number of challenging research problems mostly
related to the matcher design needs to be addressed.
 Effective representation and matching:
One of the biometric system design challenge is to
arrive at a representational/invariance model of the
identifier that is realistic. The samples used to form
these models could have possibly been acquired under
inconsistent conditions. Further, the inherent
discriminatory information in the signal needs to be
formally estimated from the samples. This becomes
more difficult in case of large-scale identification
systems, due to huge number (in millions) of
classes/identifiers.
 Performance modeling:
The inherent signal capacity issue is of utmost
complexity. It involves the interaction between the
physiological and behavioral attributes as well as the
composition of population, at different scales of time,
space.
 Characterizing
enhancement:
signal
quality
and
A particular biometric must be universal in nature, for
it to be effective. In practical use, inconsistent
presentation of the signals and adverse signal
acquisition conditions often leads to nearly unusable
biometric signals. The issue is further affected by the
underlying biometric signal varying over time due to
aging.
 Empirical Performance Measurement:
Determining reliable techniques to estimate
performance is in itself a matter of concern. Besides,
the issue related to security, privacy, aliveness and
integrity detection needs to be addressed.
8. Conclusion
In the recent years biometric authentication has gained
exciting technical improvement. It has become an
essential part of our life. In this paper we have
introduced the concept behind the authentication using
biometric, briefly explained the working of different
techniques and also estimated the effectiveness by
comparing the evaluation metrics. The idea here is not
to provide a deep knowledge of different biometric
technologies to the reader but to introduce its
effectiveness, to show how these technologies differ
from each other, applicability and finally some of the
research challenges.
Bibliography
[1] Debnath Bhattacharyya, Rahul Ranjan, Farkhod
Alisherov
A,
Minkyu
Choi,
"Biometric
Authentication: A Review," International Journal of
u- and e- Service, Science and Technology, vol. 2,
no. 3, p. 16, Sep. 2009.
[2] Fahad Al-harby, Rami Qahwaji, mumtaz kamaka,
"Secure Biometrics Authentication: A brief review
of the Literature," in School of Informatics,
Univercity of Bradford, UK, p. 7.
[3] Sourav Ganguly, Subhayan Roy Moulick, "A
Review On Different Biometric Techniques,"
International Journal of Engineering Research &
Technology (IJERT), vol. 1, no. 5, p. 7, Jul. 2012.
[4] Chun-I Fan and Yi-Hui Lin, "Provably Secure
Remote Truly Three-Factor Authentication Scheme
With Privacy Protection on Biometrics," IEEE
Transactions on Information Forensic and security,
vol. 4, no. 4, p. 13, Dec. 2009.
[5] Anil K. Jain, Fellow, Arun Ross, and Sharath
Pankanti, "Biometrics: A Tool for Information
Security," IEEE Transactions on Information
Forensic and security, vol. 1, no. 2, p. 19, Jun. 2006.
[6] Biometrics. Wikipedia, The free encyclopedia.
[Online]. http://wikipedia.org/wiki/Biometrics