New biometrics


Nov 29, 2013 (4 years and 7 months ago)




Biometric identifier classification

Biometric identifier characteristics comparison

Multimodal Biometrics

Biometric Standards

Challenges in Biometrics

Identifiable biometric

Biological traces

DNA, blood, saliva, etc.

Biological (physiological) characteristics

fingerprints, eye irises and retinas, hand palms
and geometry, and facial geometry

Behavioral characteristics

signature, gait, keystroke dynamics, lip motion,

Classification of identifiers

Physiological biometric identifiers:


hand geometry,

eye patterns (iris and retina),

facial features

and other physical characteristics.

Behavioral identifiers:



typing patterns


Analyzers based on behavioral identifiers are often
less conclusive due to limitations/complex patterns.

Example of banking application

Biometric identifiers

Courtesy of G. Bromba

Biometric Market Share

Comparison of biometric


Hand vein

Facial Thermogram

Ear print


Human eye has its own totally unique pattern of blood vessels.

Because of its internal location, the retina is protected from variations
caused by exposure to the external environment (unlike fingerprints).

Which Biometric is the Best?

(everyone should have this trait)

(everyone has a different value)

(should be invariant with time)

(can be measured quantitatively)

(achievable recognition accuracy,
resources required, operating environment)

(are people willing to accept it?)

(how easily can it be spoofed?)

Selecting a Biometric

Selecting the ‘right’ biometric is a complicated
problem that involves more factors than just
accuracy. It depends on cost, error rates,
computational speed, acquitability, privacy and easy
of use.

Ideal Biometric

The ideal biometric characteristics have five qualities:


Unchanging on an individual over time.


Showing great variation over the population.


The entire population should ideally have this
measure in multiples.


Easy to image using electronic sensors.


People do not object to having this measurement
taken on them.

Quantitative measures

Quantitative measures of these five qualities have been developed.

"Robustness" is measured by the "false non
match rate" (Type I
error), the probability that a submitted sample will not match
the enrollment image.

"Distinctiveness" is measured by the "false match rate" (Type II
error), the probability that a submitted sample will match the
enrollment image of another user.

"Availability" is measured by the "failure to enroll" rate, the
probability that a user will not be able to supply a readable
measure to the system upon enrollment.

"Accessibility" can be quantified by the "throughput rate" of the
system, the number of individuals that can be processed in a
unit time, such as a minute or an hour.

"Acceptability" is measured by polling the device users.

Biometric System Goals

A biometric system can be designed to test one of only two possible


The submitted samples are from an individual known to the

The submitted samples are from an individual not known to the

Applications to test the first hypothesis are called "positive

identification" systems while applications testing the latter are

called "negative identification" systems.

Types of Biometrics

Overt Versus Covert:

The first partition is "overt/covert". If the user is aware that a
biometric identifier is being measured, the user is overt. If unaware, the use is
covert. Almost all conceivable access control and non
forensic applications are overt.
Forensic applications can be covert.

Habituated Versus Non


applies to the intended users of the
application. Users presenting a biometric trait on a daily basis can be considered
habituated after a short period of time. Users who have not presented the trait
recently can be considered "non

Attended Versus Non
: This partition refers to whether the use of the
biometric device during operation will be observed and guided by system

Open Versus Closed:

If a system is to be open, data collection, compression and
format standards are required. A closed system can operate perfectly well on
completely proprietary formats.

Generic Biometric System

A generic biometric system.

Multimodal Biometrics

Multimodal Biometric system
is a system
that uses more than one independent or
weakly correlated biometric identifier taken
from an individual (e.g., fingerprint and face
of the same person, or fingerprints from two
different fingers of a person)

modal Systems: Fusion

Early integration or sensor fusion

Integration is performed on the feature level

Classification is done on the combined
feature vector

modal Systems: Fusion

Late integration or decision fusion

Each modality is first pre
classified independently

The final classification is based on the fusion of
the outputs of the different modalities

Multimodal biometrics

Multimodal biometrics systems

improve performance

A combination in a verification system improves
system accuracy

A combination in an identification system improves
system speed as well as accuracy

A combination of uncorrelated modalities (e.g.
fingerprint and face, two fingers of a person, etc.) is
expected to result in a better improvement in
performance than a combination of correlated
modalities (e.g. different fingerprint matchers)

Other work: classification

FBI Fingerprint card (includes information on
gender, ethnicity, height, weight, eye color
and hair color)

Wayman (1997) proposed filtering large
biometric databases based on gender and

Givens et al. (2003) and Newham (1995)
showed that age, gender and ethnicity can
affect the performance of a biometric system

International Standards

Application Programming
Interface (API)

is the automated use of
physiological or behavioral characteristics to
determine or verify an identity

Standards for interfaces
and methods for
performance evaluation are needed

Biometric Authentication

Layers of interaction with biometric authentication


Standardization of generic biometric technologies to
support interoperability and data interchange between
applications and systems

Included: common file formats, application programming
interfaces (APIs), biometric templates, template protection
techniques, related application/implementation profiles,
methodologies for conformity

Basic Standards


The most popular API in the
biometrics area


Common Biometric Exchange File

ANSI X9.84

Biometric Information
Management and Security for the Financial
Services Industry

ISO/IEC 19794

Biometric Data
Interchange Formats

Challenges in Biometrics

Large number of classes (~ 6 billion faces)

Large intra
class variability

Small inter
class variability


Noisy and distorted images

Population coverage & scalability

System performance (error rate, speed, cost)

Attacks on the biometric system

Every biometric characteristic has some limitations

Threats to Biometrics

The Modern Burglar


s Technique

Only a few dollars

worth of materials

Making the Actual Clone

You can place the

gummy finger

over your real
finger. Observers aren

t likely to detect it when you
use it on a fingerprint reader.


t try this at home! (Matsumoto)


There is wide variety of biometric identifiers that
posses different characteristics

Each biometric system should take into account the
end goal of application

biometrics improve performance of individual
matchers and is active topic of current biometric

Biometric standards are being developed, while
biometric reliability is still a concern

Reference and Links

Signal Processing Institute, Swiss Federal
Institute of Technology

Biometric Systems Lab, University of Bologna

Textbooks 1 and 2 CPSC 601.20