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Abstract
A wide variety of systems require reliable personal recognition schemes to either confirm or
determine the identity of an individual requesting their services. The purpose of such schemes is to
ensure that the rendered services are accessed only by a legitimate user, and not anyone else.
Examples of such applications include secure access to buildings, computer systems, laptops,
cellular phones and ATMs. In the absence of robust personal recognition schemes, these systems
are vulnerable to the wiles of an impostor. Biometric recognition, or simply biometrics, refers to the
automatic recognition of individuals based on their physiological and/or behavioral characteristics.
By using biometrics it is possible to confirm or establish an individual’s identity based on “who she
is”, rather than by “what she possesses” (e.g., an ID card) or “what she remembers” (e.g., a
password). In this paper, we give a brief overview of the field of biometrics and summarize some
of its advantages, disadvantages, strengths, limitations, and related privacy concerns.
Index Terms: Biometrics, Recognition, Verification, Identification, Multimodal Biometrics
1. Introduction
Humans have used body characteristics such as face, voice, gait, etc. for thousands of years to
recognize each other. Alphonse Bertillon, chief of the criminal identification division of the police
department in Paris, developed and then practiced the idea of using a number of body
measurements to identify criminals in the mid 19th century. Just as his idea was gaining popularity,
it was obscured by a far more significant and practical discovery of the distinctiveness of the
human fingerprints in the late 19th century. Soon after this discovery, many major law enforcement
departments embraced the idea of first “booking” the fingerprints of criminals and storing it in a
database (actually, a card file). Later, the leftover (typically, fragmentary) fingerprints (commonly
referred to as latents) at the scene of crime could be “lifted” and matched with fingerprints in the
database to determine the identity of the criminals. Although biometrics emerged from its extensive
use in law enforcement to identify criminals (e.g., illegal aliens, security clearance for employees
for sensitive jobs, fatherhood determination, forensics, positive identification of convicts and
prisoners), it is being increasingly used today to establish person recognition in a large number of
civilian applications.
What biological measurements qualify to be a biometric? Any human physiological and/or
behavioral characteristic can be used as a biometric characteristic as long as it satisfies the
following requirements:
Universality: each person should have the characteristic;
• Distinctiveness: any two persons should be sufficiently different in terms of the
characteristic;
• Permanence: the characteristic should be sufficiently invariant (with respect to the matching
criterion) over a period of time;
• Collectability: the characteristic can be measured quantitatively.
However, in a practical biometric system (i.e., a system that employs biometrics for personal
recognition), there are a number of other issues that should be considered, including:
• Performance, which refers to the achievable recognition accuracy and speed, the resources
required to achieve the desired recognition accuracy and speed, as well as the operational
and environmental factors that affect the accuracy and speed;
• Acceptability, which indicates the extent to which people are willing to accept the use of a
particular biometric identifier (characteristic) in their daily lives;
• Circumvention, which reflects how easily the system can be fooled using fraudulent
methods.
A practical biometric system should meet the specified recognition accuracy, speed, and resource
requirements, be harmless to the users, be accepted by the intended population, and be sufficiently
robust to various fraudulent methods and attacks to the system.
2. Biometric Systems
A biometric system is essentially a pattern recognition system that operates by acquiring biometric
data from an individual, extracting a feature set from the acquired data, and comparing this feature
set against the template set in the database. Depending on the application context, a biometric
system may operate either in verification mode or identification mode:
• In the verification mode, the system validates a person’s identity by comparing the captured
biometric data with her own biometric template(s) stored system database. In such a system,
an individual who desires to be recognized claims an identity, usually via a PIN (Personal
Identification Number), a user name, a smart card, etc., and the system conducts a one-toone
comparison to determine whether the claim is true or not (e.g., “Does this biometric
data belong to Bob?”). Identity verification is typically used for positive recognition, where
the aim is to prevent multiple people from using the same identity [26].
• In the identification mode, the system recognizes an individual by searching the templates
of all the users in the database for a match. Therefore, the system conducts a one-to-many
comparison to establish an individual’s identity (or fails if the subject is not enrolled in the
system database) without the subject having to claim an identity (e.g., “Whose biometric
data is this?”). Identification is a critical component in negative recognition applications
where the system establishes whether the person is who she (implicitly or explicitly) denies
to be. The purpose of negative recognition is to prevent a single person from using multiple
identities [26]. Identification may also be used in positive recognition for convenience (the
user is not required to claim an identity). While traditional methods of personal recognition
such as passwords, PINs, keys, and tokens may work for positive recognition, negative
recognition can only be established through biometrics.
A Comparison of Various Biometrics
A number of biometric characteristics exist and are in use in various applications (see Figure 3).
Each biometric has its strengths and weaknesses, and the choice depends on the application. No
single biometric is expected to effectively meet the requirements of all the applications. In other
words, no biometric is “optimal”. The match between a specific biometric and an application is
determined depending upon the operational mode of the application and the properties of the
biometric characteristic. A brief introduction of the commonly used biometrics is given below:
DNA: Deoxyribo Nucleic Acid (DNA) is the one-dimensional ultimate unique code for
one’s individuality - except for the fact that identical twins have identical DNA patterns. It
is, however, currently used mostly in the context of forensic applications for person
recognition. Three issues limit the utility of this biometrics for other applications: (i)
contamination and sensitivity: it is easy to steal a piece of DNA from an unsuspecting
subject that can be subsequently abused for an ulterior purpose; (ii) automatic real-time
recognition issues: the present technology for DNA matching requires cumbersome
chemical methods (wet processes) involving an expert’s skills and is not geared for on-line
non-invasive recognition; (iii) privacy issues: information about susceptibilities of a person
to certain diseases could be gained from the DNA pattern and there is a concern that the
unintended abuse of genetic code information may result in discrimination, e.g., in hiring
practices.
• Ear: It has been suggested that the shape of the ear and the structure of the cartilegenous
tissue of the pinna are distinctive. The ear recognition approaches are based on matching the
distance of salient points on the pinna from a landmark location on the ear. The features of
an ear are not expected to be very distinctive in establishing the identity of an individual.
Face: Face recognition is a non-intrusive method, and facial images are probably the most
common biometric characteristic used by humans to make a personal recognition. The
applications of facial recognition range from a static, controlled “mug-shot” verification to a dynamic, uncontrolled face identification in a cluttered background (e.g., airport). The most
popular approaches to face recognition are based on either (i) the location and shape of
facial attributes, such as the eyes, eyebrows, nose, lips, and chin and their spatial
relationships, or (ii) the overall (global) analysis of the face image that represents a face as
a weighted combination of a number of canonical faces. While the verification performance
of the face recognition systems that are commercially available is reasonable [34], they
impose a number of restrictions on how the facial images are obtained, sometimes requiring
a fixed and simple background or special illumination. These systems also have difficulty in
recognizing a face from images captured from two drastically different views and under
different illumination conditions. It is questionable whether the face itself, without any
contextual information, is a sufficient basis for recognizing a person from a large number of
identities with an extremely high level of confidence [29]. In order that a facial recognition
system works well in practice, it should automatically (i) detect whether a face is present in
the acquired image; (ii) locate the face if there is one; and (iii) recognize the face from a
general viewpoint (i.e., from any pose).
• Facial, hand, and hand vein infrared thermogram: The pattern of heat radiated by
human body is a characteristic of an individual and can be captured by an infrared camera in
an unobtrusive way much like a regular (visible spectrum) photograph. The technology
could be used for covert recognition. A thermogram-based system does not require contact
and is non-invasive, but image acquisition is challenging in uncontrolled environments,
where heat emanating surfaces (e.g., room heaters and vehicle exhaust pipes) are present in
the vicinity of the body. A related technology using near infrared imaging is used to scan
the back of a clenched fist to determine hand vein structure. Infrared sensors are
prohibitively expensive which is a factor inhibiting wide spread use of the thermograms.
• Fingerprint: Humans have used fingerprints for personal identification for many centuries
and the matching accuracy using fingerprints has been shown to be very high [25]. A
fingerprint is the pattern of ridges and valleys on the surface of a fingertip, the formation of
which is determined during the first seven months of fetal development. Fingerprints of
identical twins are different and so are the prints on each finger of the same person. Today,
a fingerprint scanner costs about US $20 when ordered in large quantities and the marginal
cost of embedding a fingerprint-based biometric in a system (e.g., laptop computer) has
become affordable in a large number of applications. The accuracy of the currently
available fingerprint recognition systems is adequate for verification systems and small- to
medium-scale identification systems involving a few hundred users. Multiple fingerprints
of a person provide additional information to allow for large-scale recognition involving
millions of identities. One problem with the current fingerprint recognition systems is that
they require a large amount of computational resources, especially when operating in the
identification mode. Finally, fingerprints of a small fraction of the population may be
unsuitable for automatic identification because of genetic factors, aging, environmental, or
occupational reasons (e.g., manual workers may have a large number of cuts and bruises on
their fingerprints that keep changing).
• Gait: Gait is the peculiar way one walks and is a complex spatio-temporal biometric. Gait is
not supposed to be very distinctive, but is sufficiently discriminatory to allow verification in
some low-security applications. Gait is a behavioral biometric and may not remain
invariant, especially over a long period of time, due to fluctuations in body weight, major
injuries involving joints or brain, or due to inebriety. Acquisition of gait is similar to acquiring a facial picture and, hence, may be an acceptable biometric. Since gait-based
systems use the video-sequence footage of a walking person to measure several different
movements of each articulate joint, it is input intensive and computationally expensive.
• Hand and finger geometry: Hand geometry recognition systems are based on a number of
measurements taken from the human hand, including its shape, size of palm, and lengths
and widths of the fingers. Commercial hand geometry-based verification systems have
been installed in hundreds of locations around the world. The technique is very simple,
relatively easy to use, and inexpensive. Environmental factors such as dry weather or
individual anomalies such as dry skin do not appear to have any negative effects on the
verification accuracy of hand geometry-based systems. The geometry of the hand is not
known to be very distinctive and hand geometry-based recognition systems cannot be scaled
up for systems requiring identification of an individual from a large population. Further,
hand geometry information may not be invariant during the growth period of children. In
addition, an individual's jewelry (e.g., rings) or limitations in dexterity (e.g., from arthritis),
may pose further challenges in extracting the correct hand geometry information. The
physical size of a hand geometry-based system is large, and it cannot be embedded in
certain devices like laptops. There are verification systems available that are based on
measurements of only a few fingers (typically, index and middle) instead of the entire hand.
These devices are smaller than those used for hand geometry, but still much larger than
those used in some other biometrics (e.g., fingerprint, face, voice).
• Iris: The iris is the annular region of the eye bounded by the pupil and the sclera (white of
the eye) on either side. The visual texture of the iris is formed during fetal development and
stabilizes during the first two years of life. The complex iris texture carries very distinctive
information useful for personal recognition. The accuracy and speed of currently deployed
iris-based recognition systems is promising and point to the feasibility of large-scale
identification systems based on iris information. Each iris is distinctive and, like
fingerprints, even the irises of identical twins are different. It is extremely difficult to
surgically tamper the texture of the iris. Further, it is rather easy to detect artificial irises
(e.g., designer contact lenses). Although, the early iris-based recognition systems required
considerable user participation and were expensive, the newer systems have become more
user-friendly and cost-effective.
• Keystroke: It is hypothesized that each person types on a keyboard in a characteristic way.
This behavioral biometric is not expected to be unique to each individual but it offers
sufficient discriminatory information to permit identity verification. Keystroke dynamics is
a behavioral biometric; for some individuals, one may expect to observe large variations in
typical typing patterns. Further, the keystrokes of a person using a system could be
monitored unobtrusively as that person is keying in information.
• Odor: It is known that each object exudes an odor that is characteristic of its chemical
composition and this could be used for distinguishing various objects. A whiff of air
surrounding an object is blown over an array of chemical sensors, each sensitive to a certain
group of (aromatic) compounds. A component of the odor emitted by a human (or any
animal) body is distinctive to a particular individual. It is not clear if the invariance in the
body odor could be detected despite deodorant smells, and varying chemical composition of
the surrounding environment.
• Palmprint: The palms of the human hands contain pattern of ridges and valleys much like
the fingerprints. The area of the palm is much larger than the area of a finger and as a result, palmprints are expected to be even more distinctive than the fingerprints. Since palmprint
scanners need to capture a large area, they are bulkier and more expensive than the
fingerprint sensors. Human palms also contain additional distinctive features such as
principal lines and wrinkles that can be captured even with a lower resolution scanner,
which would be cheaper [32]. Finally, when using a high resolution palmprint scanner, all
the features of the palm such as hand geometry, ridge and valley features (e.g., minutiae and
singular points such as deltas), principal lines, and wrinkles may be combined to build a
highly accurate biometric system.
• Retinal scan: The retinal vasculature is rich in structure and is supposed to be a
characteristic of each individual and each eye. It is claimed to be the most secure biometric
since it is not easy to change or replicate the retinal vasculature. The image acquisition
requires a person to peep into an eye-piece and focus on a specific spot in the visual field so
that a predetermined part of the retinal vasculature could be imaged. The image acquisition
involves cooperation of the subject, entails contact with the eyepiece, and requires a
conscious effort on the part of the user. All these factors adversely affect the public
acceptability of retinal biometric. Retinal vasculature can reveal some medical conditions,
e.g., hypertension, which is another factor deterring the public acceptance of retinal scanbased
biometrics.
• Signature: The way a person signs her name is known to be a characteristic of that
individual. Although signatures require contact with the writing instrument and an effort on
the part of the user, they have been accepted in government, legal, and commercial
transactions as a method of verification. Signatures are a behavioral biometric that change
over a period of time and are influenced by physical and emotional conditions of the
signatories. Signatures of some people vary substantially: even successive impressions of
their signature are significantly different. Further, professional forgers may be able to
reproduce signatures that fool the system.
• Voice: Voice is a combination of physiological and behavioral biometrics. The features of
an individual’s voice are based on the shape and size of the appendages (e.g., vocal tracts,
mouth, nasal cavities, and lips) that are used in the synthesis of the sound. These
physiological characteristics of human speech are invariant for an individual, but the
behavioral part of the speech of a person changes over time due to age, medical conditions
(such as common cold), emotional state, etc. Voice is also not very distinctive and may not
be appropriate for large-scale identification. A text-dependent voice recognition system is
based on the utterance of a fixed predetermined phrase. A text-independent voice
recognition system recognizes the speaker independent of what she speaks. A textindependent
system is more difficult to design than a text-dependent system but offers more
protection against fraud. A disadvantage of voice-based recognition is that speech features
are sensitive to a number of factors such as background noise. Speaker recognition is most
appropriate in phone-based applications but the voice signal over phone is typically
degraded in quality by the microphone and the communication channel.
A brief comparison of the above biometric techniques based on seven factors is provided in
Table 1. The applicability of a specific biometric technique depends heavily on the requirements of
the application domain. No single technique can out-perform all the others in all operational
environments. In this sense, each biometric technique is admissible and there is no optimal
biometric characteristic. For example, it is well known that both the fingerprint-based and iris- based techniques are more accurate than the voice-based technique. However, in a tele-banking
application, the voice-based technique may be preferred since it can be integrated seamlessly into
the existing telephone system.