Biometric Technology
| Face Identification Technology | ||
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Facial biometrics is one of the fastest growing areas of biometrics. With growing technologies facial recognition can convert a photograph or a video image into a code that describes a face’s physical characterizes. This can be used to identify the common person from a distance, without intruding into their personal space. Computer software for facial identification reads the peaks and valleys of an individual’s facial features; these peaks and valleys are known as nodal points. There are 80 nodal points in a human face, but the software needs only 15-20 to make an identification. Specialists concentrate on the golden triangle region between the temples and the lips. This area of the face remains the same even if hair and a beard is grown, weight is gained, aging occurs, or glasses are put on. |
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| Fingerprint Identification Technology | ||
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Principles of fingerprint biometrics
A fingerprint is made of a a number of ridges and valleys on
the surface of the finger. Ridges are the upper skin layer
segments of the finger and valleys are the lower segments.
The ridges form so-called minutia points: ridge endings
(where a ridge end) and ridge bifurcations (where a ridge
splits in two). Many types of minutiae exist, including dots
(very small ridges), islands (ridges slightly longer than
dots, occupying a middle space between two temporarily
divergent ridges), ponds or lakes (empty spaces between two
temporarily divergent ridges), spurs (a notch protruding
from a ridge), bridges (small ridges joining two longer
adjacent ridges), and crossovers (two ridges which cross
each other).
The uniqueness of a fingerprint can be determined by the
pattern of ridges and furrows as well as the minutiae
points. There are five basic fingerprint patterns: arch,
tented arch, left loop, right loop and whorl. Loops make up
60% of all fingerprints, whorls account for 30%, and arches
for 10%.
Fingerprints are usually considered to be unique, with no
two fingers having the exact same dermal ridge
characteristics. |
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| Hand Geometry Identification Technology | ||
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Hand geometry
is a biometric that identifies users by the shape of their
hands. Hand geometry readers measure a user's hand along
many dimensions and compare those measurements to
measurements stored in a file.
Viable hand
geometry devices have been manufactured since the early
1980s, making hand geometry the first biometric to find
widespread computerized use. It remains popular; common
applications include access control and time-and-attendance
operations.
Since hand
geometry is not thought to be as unique as fingerprints or
irises, fingerprinting and iris recognition remain the
preferred technology for high-security applications. Hand
geometry is very reliable when combined with other forms of
identification, such as identification cards or personal
identification numbers. In large populations, hand geometry
is not suitable for so-called one-to-many applications, in
which a user is identified from his biometric without any
other identification. |
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| Keystroke Dynamics Identification Technology | ||
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The behavioral
biometric of Keystroke Dynamics uses the manner and rhythm
in which an individual types characters on a keyboard or
keypad. The keystroke rhythms of a user are measured to
develop a unique biometric template of the users typing
pattern for future authentication. Raw measurements
available from most every keyboard can be recorded to
determine Dwell time (the time a key pressed) and Flight
time (the time between “key down” and the next “key down”
and the time between “key up” and the next “key up”). The
recorded keystroke timing data is then processed through a
unique neural algorithm, which determines a primary pattern
for future comparison.
Data needed to analyze keystroke
dynamics is obtained by keystroke logging. Normally, all
that is retained when logging a typing session is the
sequence of characters corresponding to the order in which
keys were pressed and timing information is discarded. When
reading email, the receiver cannot tell from reading the
phrase "I saw 3 zebras!" whether:
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that was typed rapidly or slowly
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| Palm Vein Identification Technology | ||
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Principles of palm vein biometrics
The pattern of blood veins is unique to every individual,
even among identical twins. Palms have a broad and
complicated vascular pattern and thus contain a wealth of
differentiating features for personal identification.
Furthermore, it will not vary during the person's lifetime.
It is a very secure method of authentication because this
blood vein pattern lies under the skin. This makes it almost
impossible for others to read or copy.
An individual's vein pattern image is captured by radiating
his/her hand with near-infrared rays. The reflection method
illuminates the palm using an infrared ray and captures the
light given off by the region after diffusion through the
palm. The deoxidized hemoglobin in the in the vein vessels
absorbs the infrared ray, thereby reducing the reflection
rate and causing the veins to appear as a black pattern.
This vein pattern is then verified against a preregistered
pattern to authenticate the individual.
As veins are internal in the body and have a wealth of
differentiating features, attempts to forge an identity are
extremely difficult, thereby enabling a high level of
security. In addition, the sensor of the palm vein device
can only recognize the pattern if the deoxidized hemoglobin
is actively flowing within the individual's veins.
This system is not dangerous, a near infrared is a component
of sunlight: there is no more exposure when scanning the
hand than by walking outside in the sun. |
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| Iris Recognition Technology | ||
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Iris
recognition
is a method of biometric authentication that uses pattern
recognition techniques based on high-resolution images of
the irides of an individual's eyes. Not to be confused with
another less prevalent ocular-based technology, retina
scanning, iris recognition uses camera technology, and
subtle IR illumination to reduce specular reflection from
the convex cornea to create images of the detail-rich,
intricate structures of the iris. These unique structures
converted into digital templates, provide mathematical
representations of the iris that yield unambiguous positive
identification of an individual.
Iris
recognition efficacy is rarely impeded by glasses or contact
lenses. Iris technology has the smallest outlier (those who
cannot use/enroll) group of all biometric technologies. The
only biometric authentication technology designed for use in
a one-to many search environment, a key advantage of iris
recognition is its stability, or template longevity as,
barring trauma, a single enrollment can last a lifetime. |
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| Retina Scan Technology | ||
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The human
retina is a thin tissue composed of neural cells that is
located in the posterior portion of the eye. Because of the
complex structure of the capillaries that supply the retina
with blood, each person's retina is unique. The network of
blood vessels in the retina is so complex that identical
twins do not even share a similar pattern.
Although
retinal patterns may be altered in cases of diabetes,
glaucoma, retinal degenerative disorders or cataracts, the
retina typically remains unchanged from birth until death.
Due to its unique and unchanging nature, the retina appears
to be the most precise and reliable biometric. Advocates of
retinal scanning have concluded that it is so accurate that
its error rate is estimated to be only one in a million.
A biometric
identifier known as a retinal scan is used to map the
unique patterns of a person's retina. The blood vessels
within the retina absorb light more readily than the
surrounding tissue and are easily identified with
appropriate lighting. A retinal scan is performed by casting
an undetectable ray of low-energy infrared light into a
person’s eye as they look through the scanner's eyepiece.
This beam of light outlines a circular path on the retina.
Because retinal blood vessels are more sensitive to light
than the rest of the eye, the amount of reflection
fluctuates. The results of the scan are converted to
computer code and stored in a database. |
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| Signature Recognition Technology | ||
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Biometric signature recognition systems will measure and analyze the physical activity of signing, such as the stroke order, the pressure applied and the speed. Some systems may also compare visual images of signatures, but the core of a signature biometric system is behavioral, i.e. how it is signed rather than visual, i.e. the image of the signature. |
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| Speaker Identification Technology | ||
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Speaker
recognition
(also known as voice recognition) is the computing
task of recognizing people (which may involve identifying
them and/or authenticating their identity) from their
voices. Such systems extract features from speech, model
them, and use them to recognize the person from his/her
voice.
Note that
there is a difference between speaker recognition
(recognizing who is speaking) and speech
recognition (recognizing what is being said).
These two terms are frequently confused, as is voice
recognition. Voice recognition is a synonym for speaker,
and thus not speech, recognition.
Speaker
recognition
has a history dating back some four decades, where the
output of several analog filters was averaged over time for
matching. Speaker recognition uses the acoustic features of
speech that have been found to differ between individuals.
These acoustic patterns reflect both anatomy (e.g., size and
shape of the throat and mouth) and learned behavioral
patterns (e.g., voice pitch, speaking style). This
incorporation of learned patterns into the voice templates
(the latter called "voiceprints") has earned speaker
recognition its classification as a "behavioral biometric." |
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| Odor Identification Technology | ||
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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. Methods of capturing
a person’s smell are being explored by Mastiff Electronic
Systems. Each human smell is made up of chemicals known as
volatiles. They are extracted by the system and converted
into a template. The use
of body odor sensors brings up the privacy issue as the body
odor carries a significant amount of sensitive personal
information. It is possible to diagnose some diseases or
activities in the
last hours (like sex, for example) by analyzing the body
odor. |
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| DNA Identification Technology | ||
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Deoxyribonucleic acid (DNA) Biometrics could be the most exact form of identifying any given individual (Baird, S., 2002). Every human being has its own individual map for every cell made, and this map, or ‘blueprint’ as it more often is called, can be found in every body cell. Because DNA is the structure that defines who we are physically and intellectually, unless an individual is an identical twin, it is not likely that any other person will have the same exact set of genes (Philipkoski, K., 2004). DNA can be collected from any number of sources: blood, hair, finger nails, mouth swabs, blood stains, saliva, straws, and any number of other sources that has been attached to the body at some time. DNA matching has become a popular use in criminal trials, especially in proving rape cases (Landers, E., 1992). The main problems surrounding DNA biometrics is that it is not a quick process to identify someone by their DNA. The process is also a very costly one (Baird, S., 2002). DNA Biometrics is not a fool proof method of identification. If forensic scientists to not conduct a DNA test properly, a person’s identification code can be skewed. Another problem is matching prior DNA samples to new samples; this is a bigger problem in DNA fingerprinting. The information looks like a bar code, and if not closely inspected an incorrect match could be made (SAIC, 2004). |
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| Human Gait Identification Technology | ||
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Gait biometrics identifies a person by the way the walk, run, or any other type of motion of the legs. A person’s gait is the way in which they move on their feet. Gait biometrics can be used to identify everything from the length and thickness of an individuals legs to the stride of their step. Unlike some other, more researched and identifiable methods of biometrics, gait biometric technology faces the difficulty of identifying not only a particular body part but a motion (World Information, 2003).
At Gait biometrics would be particularly beneficial in identifying criminal suspects. Police could scan a large crowd for a suspect without them knowing they were on to them. Gait biometrics can also be used to identify shoplifters-particularly ‘pregnant’ women. Women pretending to be pregnant will walk differently then women who are actually pregnant. This would be a large advance in technology if introduced to common retail stores. Some sources recognize what gait biometrics says it can do, but doubts it’s ability to perform. A gait system can easily be deceived because walking patterns can be sometimes be altered. Skeptics also doubt gait biometrics ability to perform in real life scenarios, such as airports and large crowds. Regardless of what critics say, gait biometrics will have to prove it’s capabilities in action. |
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| Human Ear Canal Identification Technology | ||
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It is
known from prior art that the acoustical properties of the
ear can be used to identify people uniquely. This kind of
biometric feature cannot easily be copied, and can easily be
implemented in a mobile phone for remote identification,
thus replacing conventional, less reliable methods of
identification such as' the PIN code. In the case
of acoustic ear canal biometrics, what is of interest is the
topology of the ear canal, which is unique for every human.
An incoming sound signal is reflected and otherwise modified
by the ear canal to give an aurally reflected signal which
exits the ear canal. A sound
signal is directed into the ear of a user, and the frequency
response of the ear canal is measured and analyzed to
extract a feature vector unique to this user. However, since
the microphone used to detect the response from the ear
canal must also pick up any surrounding sound signals, such
a measurement system is particularly prone to error owing to
background noise. These unwanted background noise signals
can really only be excluded from the measurement described
by, for example, enclosing the microphone and the ear in
headphones of a size large enough to encompass the entire
ear. Since such headphones are generally cumbersome to use
and awkward to transport, they are impractical for frequent
use, and unsuited to user identification for applications
such as telephone banking, telephone brokerage, etc, which a
user generally wishes to carry out with a mobile phone,
whether at home or underway. |
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