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An Exploratory Study Of Odor Biometrics Modality For Human Recognition
1Oyeleye, C. A., 2Fagbola T. M, 3Babatunde R. S, 4Adigun A. A
1,2,4Department of Computer Engineering and Technology, Ladoke Akintola University of
Technology, Ogbomoso, Oyo State.
3Department of Computer, Library and Information Science, Kwara State University, Malete,
Kwara State.
ABSTRACT
The currently recurring and alarming global
security challenges have led to the development and
use of biometric modalities for access control and
human recognition. Though a number of biometrics
have been proposed, researched and evaluated for
human recognition and access control applications; it
becomes evident that each biometrics has its strengths
and limitations as each best fit to a particular
identification / security application. Thus, there is not
one biometric modality that is perfect for all
implementations. This opens a wide gap for the
introduction and application of some newly emerging
biometric modalities for human recognition. However,
in security systems, biometrics commonly implemented
or studied include fingerprint, face, iris, voice,
signature and hand geometry. Whereas, a number of
newly emerging biometric modalities including Gait,
Vein, DNA, Body Odor, Ear Pattern, Keystroke and
Lip, promising to provide better result in terms of
performance, acceptability and circumvention are less
studied, understood, researched or implemented for
security applications. Body odor is one of the physical
characteristics of a human that can be used to identify
people. While body odor, which significantly exhibits
strong security potentials over other recently emerging
modalities, could prove very effective for accurate
personal identification, little is known about its
fundamental features and suitability for human
recognition. Sequel to this, this paper carries out an
exploratory study of odor biometrics modality for
human recognition.
Keywords: Odor biometrics, Human recognition
1. Introduction
Biometrics is the science of measuring physical
properties of living beings [4]. It can be defined as any
measurable, robust, distinctive physical characteristic
or personal trait that can be used to identify, or verify
the claimed identity of, an individual [2]. However, a
biometric template is a digital representation of an
individual‟s distinct characteristics, representing
information extracted from a biometric sample.
Biometric templates are what are actually compared in
a biometric recognition system.
Biometrics are being used in many locations to
enhance the security and convenience of the society.
Biometrics commonly implemented or studied include
fingerprint, face, iris, voice, signature and hand
geometry. Other biometric strategies are being
developed like those based on gait, retina, hand veins,
ear canal, facial thermo gram, DNA, odor and palm
prints [4]. Though, a number of biometric modalities
have been well studied, little is known about odor
biometric system. An odor is caused by one or more
volatilized chemical compounds, generally at a very
low concentration, that humans or other animals
perceive by the sense of olfaction [1].
The body odor biometrics is based on the fact that
virtually each human smell is unique. The smell is
captured by sensors that are capable to obtain the odor
from non-intrusive parts of the body such as the back of
the hand or armpit [1].
Body odor recognition is a contactless physical
biometric that attempts to confirm a person‟s identity
International Journal of Engineering Research & Technology (IJERT)
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by analyzing the olfactory properties of the human
body scent [2]. Odor biometric system has been
identified by a number of researchers as a viable system
for personal identification [3]. The evaluation of odor
characteristics and features is an important step to
implementing odor as a personal identification and
security system. In this paper, an exploratory study of
odor biometric modality for human recognition is
investigated and presented. It is organized as follows:
section 2 discusses the biometrics for human
recognition; section 3 explicitly examines the human
body odor and associated issues while conclusion is
drawn in section 4.
2. Biometrics for Human Recognition
The increased need of privacy and security in our
daily life has given birth to this new area of science and
technology [4]. Biometrics involves a set of approaches
for uniquely recognizing humans based upon one or
more intrinsic physical or behavioral traits. In computer
science, biometrics is used as a form of identity access
management and control. It is also used to identify
individuals in groups that are under surveillance.
Biometric identifiers are the distinctive, measurable
characteristics used to identify individuals [5]. The two
categories of biometric identifiers include physiological
and behavioral characteristics [6]. Physiological
characteristics are related to the shape of the body and
include but are not limited to: fingerprint, face
recognition, DNA, palm print, hand geometry, iris
recognition, and odor. However, behavioral
characteristics are related to the behavior of a person,
including but not limited to: typing rhythm, gait and
voice. Biometrics works by unobtrusively matching
patterns of live individuals in real time against enrolled
records. Leading examples are biometric technologies
that recognize and authenticate faces, hands, fingers,
signatures, irises, voices and fingerprints
Fig.1: Examples of Various Biometric Characteristics:
(a) DNA, (b) Ear,(c) Face, (d) Facial Thermogram,
(e) Hand Thermogram, (f) Hand Vein, (g) Fingerprint,
(h) Gait, (i) Hand Geometry, (j) Iris, (k) Palmprint,
(l) Retina, (m) Signature
Source: Rishabh & Sandeep (2012)
2.1 Functional Properties of Biometric Traits
Certain factors are to be considered when assessing the
suitability of any trait for use in biometric
authentication. [7] identified some factors to include
universality, uniqueness, measurability, performance,
acceptability and circumvention.
However, [4] argued that the requirements of a
biometric feature are uniqueness, universality,
permanence, measurability, user friendliness,
collectible and acceptability.
(i) universality means that every person should have the
characteristic,
(ii) uniqueness indicates that no two persons should be
the same in terms of the characteristic,
(iii) permanence means that the characteristic should be
invariant with time and environment
(iv) collectability indicates that the characteristic can
be measured quantitatively.
In practice, there are some other important
requirements:
(i) performance, which refers to the achievable
identification accuracy, the resource requirements to
achieve an acceptable identification accuracy, and the
working or environmental factors that affect the
identification accuracy,
(ii) acceptability, which indicates to what extent
people are willing to accept the biometric system, and
(iii) circumvention, which refers to how easy it is
to fool the system by fraudulent techniques.
2.2 Components of all Biometric Systems
A modern biometric system consists of six
modules: sensors, aliveness detection, quality checker,
feature-generator, matcher and decision modules [14].
Sensors, which are the most important part of a
„biometric capture device‟, target physical properties of
body parts, or physiological and behavioral processes,
which are called „biometric characteristics‟. The output
of the sensor(s) is an analogical, or digital,
representation of the biometric characteristic, this
representation is called a „biometric sample‟.
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Fig 2: A Generic Biometrics System
Source: Nalini et al (2000)
3. The Human Body Odor
Every human body exudes an odor that
characterizes its chemical composition and which could
be used for distinguishing various individuals [9]. That
means, body odor is one of the physical characteristics
of a human that can be used to identify people. The
human odor is released from various parts of body and
exists in various forms such as exhalation, armpits,
urine, stools, farts or feet [3].
The study of odors is a growing field but is a
complex and difficult one. The complexity of odors
arises from the sensory nature of smell [3]. The
perception of odor sensation is hard to investigate
because exposure to a volatile chemical elicits a
different response based on sensory and physiological
signals, and interpretation of these signals influenced
by experience, expectations, personality or situational
factors.
Odors are mixtures of light and small molecules
that, coming in contact with various human sensory
systems, also at very low concentrations in the inhaled
air, are able to stimulate an anatomical response: the
experienced perception is the odor [10].
Body odor serves several functions including
communication, attracting mates, assertion of territorial
rights, and protection from a predator [3]. A
component of the odor emitted by a human body is
distinctive to a particular individual. It is not clear if the
invariance in a body odor could be detected despite
deodorant smells, and varying chemical composition of
the surrounding environment [9]. The odor biometric
modality can been applied to many industrial
applications including indoor air quality, health care,
safety, security, environmental monitoring, quality
control of beverage/food products and food processing,
medical diagnosis, psychoanalysis, agriculture,
pharmaceuticals, biomedicine, military applications,
aerospace, detection of hazardous gases and chemical
warfare agents [10].
3.1 Human Body Odor Acquisition Analysis
The human odor is released from various parts
of body and exists in various forms such as exhalation,
armpits, urine, stools, farts or feet [3]. Each chemical of
the human odor is extracted by the biometric system
and converted into a unique data string. The quality
checker module performs a quality check on biometric
samples and indicates whether the characteristic should
be sensed again. Also, the quality check module may
become responsible for producing extra data if the
system is set for accepting only high resolution
samples.
The most important element of a quality
metric is its utility. Biometric samples with the highest
resolution do not necessarily result in a better
identification, while they always result in being
redundant [16]. The feature-generator module extracts
discriminatory features from biometric samples and
generates a digital string called „biometric features‟. A
whole set of these features then constitute the
„biometric template‟.
Templates could be used to recreate artifacts that might
be exploited for spoofing the system; such a possibility
should be prevented by using encrypted templates. It is
important that compressed biometric samples are not
stored in the system or included in the template
together with template encryption as this measure is
vital to avoid the main risks of template misuse (e.g.
identity theft, data mining and profiling) [16].
The matcher module compares the template
with one or more templates previously stored. The
decision module takes the final decision about personal
identity according to the system‟s threshold for
acceptable matching. Extra data can hardly be
generated by these two modules; their ethical and
privacy relevance chiefly concerns the setting of the
threshold for acceptable matching, which is not a trivial
fact because it determines false rejections and false
acceptance rates [16].
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Fig 3: A Typical Odor Biometric Identification System
Source: Natale et al (2000)
Fig 4: Odor Biometric Training and Testing System for
identification
Source: Natale et al (2000)
3.2 Odor Detection and Classification
Despite the importance of perception of odor and
flavor, there are problems in comparing different
persons„experience of smell and in quantifying odor.
This need has created a need for a more analytical
approach to the quantitative measurement of odor. For
this purpose, the field of instrumental analyzers such as
Electronic Noses (E-Noses) and Olfaction Systems
(Machine Olfaction) has been developed in response to
this need [13].
3.2.1 Electronic Noses (E-Noses)
Electronic/artificial noses are being developed as a
system for the automated detection and classification of
odors, vapors and gases. E-Nose is represented as a
combination of two components: sensing system and
pattern recognition system.
Fig 5: Schematic Diagram of E-Nose
Source: Zhanna (2005)
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Fig 6: Schematic diagram of the lab-made E-nose system
Source: Chatchawal et al (2009)
3.2.1.1 Sensing System
Sensing system allows tracing the odor from the
environment. This system can be single sensing device,
like gas chromatograph and spectrometer. In this case it
produces an array of measurements for each
component. The second type of sensing system is an
array of chemical sensors. It is more appropriate for
complicative mixtures because each sensor measure a
different property of the sensed chemical [13]. Hybrid
of single sensing device and array of chemical sensors
is also possible. Each odorant presented to the sensing
system produces a characteristic pattern of the odorant.
By presenting a mass of sundry odorants to this system
a database of patterns is built up and used to construct
the odor recognition system.
3.2.1.2 Pattern Recognition System
Pattern recognition system is the second component of
electronic nose used for odor recognition. Its goal is to
train or to build the recognition system to produce
unique classification or clustering of each odorant
through the automated identification [3]. Unlike
human systems, electronic noses are trained to identify
only a few different odors or volatile compounds. There
is a very strong restriction to use these noses for human
recognition. State-of-the-art approaches do not make it
possible to identify all components of the human body
precisely. As such, recognition process incorporates
several approaches: Statistical, ANN and
Neuromorphic.
Many of the statistical techniques are complementary
to ANNs and are often combined with them to produce
classifiers and clusters. It includes PCA, partial least
squares, discriminant and cluster analysis [16]. PCA
breaks apart data into linear combinations of orthogonal
vectors based on axes that maximize variance. To
reduce the amount of data, only the axes with large
variances are kept in the representation [17]. When an
ANN is combined with the sensor array, the number of
detectable chemicals is generally greater than the
number of unique sensor types. A supervised approach
involves training a pattern classifier to relate sensor
values to specific odor labels. An unsupervised
algorithm does not require predetermined odor classes
for training. It essentially performs clustering of the
data into similar groups based on the measured
attributes or features [17].
3.2.2 Olfactory Signal Processing
The goal of an electronic nose is to identify an odorant
sample and to estimate its concentration in human
recognition case. It means signal processing and pattern
recognition system. However, those two steps may be
subdivided into preprocessing, feature extraction,
classification and decision-making [13]. But first, a
database of expected odorants must be compiled, and
the sample must be presented to the nose‟s sensor
array.
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Fig 7: Signal Processing and Pattern Recognition systems stages
Source: Zhanna (2005)
The signal processing and pattern recognition
are explicitly discussed below [13]:
A. Preprocessing
Preprocessing compensates for sensor drift,
compresses the response of the sensor array and
reduces sample-to-sample variations. Typical
techniques include: normalization of sensor response
ranges for all the sensors in an array; and compression
of sensor transients.
B. Feature extraction
Feature extraction has two purposes: to reduce
the dimensionality of the measurement space, and to
extract information relevant for pattern recognition.
Feature extraction is generally performed with linear
transformations such as the classical PCA.
C. Classification
The commonly used method for performing
the classification task is artificial neural networks
(ANNs). An artificial neural network is an information
processing system that has certain performance
characteristics in common with biological neural
networks. It allows the electronic nose to function in
the way similar to brain function when it interprets
responses from olfactory sensors in the human nose.
D. Decision Making
The classifier produces an estimate of the class for an
unknown sample along with an estimate of the
confidence placed on the class assignment. A final
decision-making stage may be used if any application-
specific knowledge is available, such as confidence
thresholds or risk associated with different
classification errors. The decision-making module may
modify the classifier assignment and even determine
that the unknown sample does not belong to any of the
odorants in the database.
3.3 Odor Quantitative Analysis Metrics
Different aspects of odor can be measured
through a number of quantitative methods including
concentration and apparent intensity assessment.
3.3.1 Odor Concentration
An olfactometer test is used to measure odor
concentration, which employs a panel of human noses
as sensors [12]. In the olfactometry testing procedure, a
diluted odorous mixture and an odor-free gas are
presented separately from sniffing ports to a group of e-
noses, kept in an odor-neutral room. The gases emitted
from each sniffing port are compared, after which the
presence of odor is determined alongside the
confidence level such as guessing, inkling, or certainty
of their assessment. The gas-diluting ratio is then
decreased by a factor of two (i.e. chemical
concentration is increased by a factor of two). This
process is repeated and continues for a number of
dilution levels [12]. The responses of the e-noses over a
range of dilution settings are used to calculate the
concentration of the odor in terms of European Odor
Units (ouE/m³). The main panel calibration gas used is
Butan-1-ol, which at a certain diluting gives 1 ouE/m³
[12]. The concentration is expressed as the dilution
required for achieving panel detection threshold.
Mathematically, the concentration is expressed as [10].
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where C is the odour concentration, V0 the volume of
odorous sample and Vf the volume of odour-free air
required to reach the threshold.
By analogy, for a dynamic olfactometer the
concentration is given by (Magda, 2011):
3.3.2 Odor Intensity
Odor intensity is the perceived strength of
odor sensation. This intensity property is used to locate
the source of odors and perhaps most directly related to
odor nuisance [18]. Perceived strength of the odor
sensation is measured in conjunction with odor
concentration. This can be modeled by the Weber-
Fechner law [11].
I = a * log(c)+b where
I is the perceived psychological intensity at the dilution
step on the butanol scale, a is the Weber-Fechner
coefficient, C is the chemical concentrations and b is
the intercept constant (0.5 by definition). Odor intensity
can be expressed using an odor intensity scale, which is
a verbal description of an odor sensation to which a
numerical value is assigned (Jiang, 2006).
Odor intensity can be divided into the
following categories according to intensity: 0 - no odor,
1 - very weak (odor threshold), 2 – weak, 3 – distinct, 4
– strong, 5 - very strong and 6 – intolerable
3.4 Odor Biometrics Performance Metrics
The following parameters are generally used
to measure the efficiency of a biometric system
(Henshaw et al, 2006):
3.4.1 False Acceptance Rate (FAR)
The FAR is the frequency that a non
authorized person is accepted as authorized. Because a
false acceptance can often lead to damages, FAR is
generally a security relevant measure. FAR is a non-
stationary statistical quantity which does not only show
a personal correlation, it can even be determined for
each individual biometric characteristic (called personal
FAR).
Due to the statistical nature of the false acceptance rate,
a large number of fraud attempts have to be undertaken
to get statistical reliable results. The fraud trial can be
successful or unsuccessful. The probability for success
FAR(n) against a certain enrolled person n is measured:
These values are more reliable with more independent
attempts per person/characteristic. In this context,
independency means that all fraud attempts have to be
performed with different persons or characteristics! The
overall FAR for N participants is defined as the average
of all FAR(n):
The values are more accurate with higher numbers of
different participants/characteristics (N). Usually,
during FAR determination, a fraud attempt is an attack
using the characteristics of non-authorized persons.
This, however, presents a high security which may not
be present since there are a lot of further possibilities
for promising attacks. A fraud attempt is successful if
the user interface of the application provides a
"successful" message or if the desired access is granted.
A fraud attempt counts as rejected if the user interface
of the application provides an "unsuccessful" message.
In cases where no "unsuccessful" message is available,
a verification time interval has to be given to ensure
comparability. If the verification time interval has
expired the fraud attempt is counted unsuccessful.
3.4.2 False Rejection Rate (FRR)
The FRR is the frequency that an authorized person is
rejected access. FRR is generally thought of as a
comfort criteria, because a false rejection is most of all
annoying. FRR is a non-stationary statistical quantity
which does not only show a strong personal correlation,
it can even be determined for each individual biometric
characteristic (called personal FRR).
Due to the statistical nature of the false rejection rate, a
large number of verification attempts have to be
undertaken to get statistical reliable results. The
verification can be successful or unsuccessful. In
determining the FRR, only fingerprints from
successfully enrolled users are considered.
The probability for lack of success (FRR(n)) for a
certain person is measured:
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These values are better with more independent attempts
per person/feature. The overall FRR for N participants
is defined as the average of FRR(n):
The values are more accurate with higher numbers of
participants (N). The determined FRR includes both
poor picture quality and other rejection reasons such as
finger position, rotation, etc. in the reasons for
rejection. In many systems, however, rejections due to
bad quality are generally independent of the threshold.
A verification attempt is successful if the user interface
of the application provides a "successful" message or if
the desired access is granted. A verification attempt
counts as rejected if the user interface of the application
provides an "unsuccessful" message. In cases of no
reaction, a verification time interval has to be given to
ensure comparability. If the time interval has expired
the verification attempt is counted unsuccessful.
3.4.3 Failure to Enrol rate (FTE, also FER)
The FER is the proportion of people who fail to be
enrolled successfully. FER is a non-stationary
statistical quantity which does not only show a strong
personal correlation, but can even be determined for
each individual biometric characteristic (called personal
FER). Those who are enrolled yet but are mistakenly
rejected after many verification/identification attempts
count for the Failure to Acquire (FTA) rate. The FTA
usually is considered within the FRR and need not be
calculated separately.
3.4.4 False Identification Rate (FIR)
The False Identification Rate is the probability in an
identification that the biometric features are falsely
assigned to a reference. The exact definition depends
on the assignment strategy; namely, after feature
comparison, often more than one reference will exceed
the decision threshold.
3.4.5 Relative Operating Characteristic (ROC)
In general, the matching algorithm performs a decision
using some parameters (e.g. a threshold). In biometric
systems, the FAR and FRR can typically be traded off
against each other by changing those parameters. The
ROC plot is obtained by graphing the values of FAR
and FRR, changing the variables implicitly.
Fig 8: Receiver Operating Curve (ROC)
Source: Nalini et al (2000)
3.4.6 Equal Error Rate (EER)
This is the rate at which both accept and reject errors
are equal. ROC plotting is used because how FAR and
FRR can be changed, is shown clearly. When quick
comparison of two systems is required, the EER is
commonly used. Obtained from the ROC plot by taking
the point where FAR and FRR have the same value.
The lower the EER, the more accurate the system is
considered to be.
3.4.7 Failure to Capture Rate (FTC)
Within automatic systems, the probability that the
system fails to detect a biometric characteristic when
presented correctly is generally treated as FTC.
3.4.8 Template Capacity
It is defined as the maximum number of sets of data
which can be input in to the system.
4 Conclusion
This study explicitly and theoretically
analyzes odor biometric system for human recognition.
It presents a comprehensive analysis of the technical,
design and implementation issues relative to the
application of odor biometric features for human
recognition. The knowledge unveiled in this study will
assist security system developers to understand the
properties of odor biometric systems, its strengths and
weaknesses as a unified biometric system or as a
system to be multi-modally combined with other
biometric modality (ies) to realize a more robust human
recognition system.
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