typing rhythm, gait, and voice. Some researchers have coined the term

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Biometric identification

Biometrics refers to the identification of humans by their characteristics or
traits. Biometrics is used in computer science as a form of identification and
access control. It is also used to identify individuals in groups that are under

Biometric identifiers are the distinctive, measurable characteristics used to
label and describe individuals. Biometric identifiers are often categorized as
physiological versus behavioral characteristics. A physiological biometric
would identify by one's voice, DNA, hand print or behavior. Behavioral
biometrics are related to the behavior of a person, including but not limited to:
typing rhythm, gait, and voice. Some researchers have coined the term
behaviometrics to describe the latter class of biometrics.

More traditional means of access control include token
based identification
systems, such as a driver's license or passport, and knowledge
identification systems, such as a password or personal identification number.
Since biometric identifiers are unique to individuals, they are more reliable in
verifying identity than token and knowledge
based methods; however, the
collection of biometric identifiers raises privacy concerns about the ultimate
use of this information.

A fingerprint in its narrow sense is an impression left by the friction ridges of a human
finger.[1] In a wider use of the term, fingerprints are the traces of an impression from the
friction ridges of any part of a human or other primate hand. A print from the foot can also
leave an impression of friction ridges. A friction ridge is a raised portion of the epidermis
on the digits (fingers and toes), the palm of the hand or the sole of the foot, consisting of
one or more connected ridge units of friction ridge skin.[1] These are sometimes known as
"epidermal ridges" which are caused by the underlying interface between the dermal
papillae of the dermis and the interpapillary (rete) pegs of the epidermis. These epidermal
ridges serve to amplify vibrations triggered, for example, when fingertips brush across an
uneven surface, better transmitting the signals to sensory nerves involved in fine texture
perception.[2] These ridges may also assist in gripping rough surfaces and may improve
surface contact in wet conditions.[3]

Impressions of fingerprints may be left behind on a surface by the natural secretions of
sweat from the eccrine glands that are present in friction ridge skin, or they may be made
by ink or other substances transferred from the peaks of friction ridges on the skin to a
relatively smooth surface such as a fingerprint card.[4] Fingerprint records normally
contain impressions from the pad on the last joint of fingers and thumbs, although
fingerprint cards also typically record portions of lower joint areas of the fingers.


Finger vein recognition is a method of biometric authentication that uses pattern
recognition techniques based on
images of human finger vein patterns beneath the skin's surface. Finger vein recognition is one of many forms of
biometrics used to identify individuals and verify their identity.

Finger Vein ID is a biometric authentication system that matches the vascular pattern in an individual's finger to
previously obtained data. Hitachi developed and patented a finger vein ID system in 2005.[1] The technology is
currently in use or development for a wide variety of applications, including credit card authentication, automobile
security, employee time and attendance tracking, computer and network authentication, end point security and
automated teller machines.

To obtain the pattern for the database record, an individual inserts a finger into an attester terminal containing a
infrared LED (light

emitting diode) light and a monochrome CCD (charge
coupled device) camera. The
hemoglobin in the blood absorbs near
infrared LED light, which makes the vein system appear as a dark pattern
of lines. The camera records the image and the raw data is digitized, certified and sent to a database of registered
images. For authentication purposes, the finger is scanned as before and the data is sent to the database of
registered images for comparison. The authentication process takes less than two seconds.[2]

Blood vessel patterns are unique to each individual, as are other biometric data such as fingerprints or the
patterns of the iris. Unlike some biometric systems, blood vessel patterns are almost impossible to counterfeit
because they are located beneath the skin's surface. Biometric systems based on fingerprints can be fooled with a
dummy finger fitted with a copied fingerprint; voice and facial characteristic
based systems can be fooled by
recordings and high
resolution images. The finger vein ID system is much harder to fool because it can only
authenticate the finger of a living person.[3]

Finger vein recognition

Iris recognition

Iris recognition is an automated method of biometric identification that uses mathematical pattern
techniques on video images of the irides of an individual's eyes, whose complex random patterns are unique and
can be seen from some distance.

Not to be confused with another, less prevalent, ocular
based technology, retina scanning, iris recognition uses
camera technology with subtle infrared illumination to acquire images of the detail
rich, intricate structures of the
iris. Digital templates encoded from these patterns by mathematical and statistical algorithms allow the
identification of an individual or someone pretending to be that individual.[1] Databases of enrolled templates are
searched by matcher engines at speeds measured in the millions of templates per second per (single
core) CPU,
and with infinitesimally small False Match rates.

Many millions of persons in several countries around the world have been enrolled in iris recognition systems, for
convenience purposes such as passport
free automated border
crossings, and some national ID systems based
on this technology are being deployed. A key advantage of iris recognition, besides its speed of matching and its
extreme resistance to False Matches, is the stability of the iris as an internal, protected, yet externally visible
organ of the eye.

In 1987 two Ophthalmology Professors, Leonard Flom, M.D.(NYU) and Aran Safir,M.D.(U.Conn), were issued a
first of its kind, broad patent # 4,641,349 entitled "Iris Recognition Technology." Subsequently, John
Daugman,PhD (Harvard Computer Science faculty) was then salaried by both ophthalmologists to write the
algorithm for their concept based upon an extensive series of high resolution iris photos supplied to him by
Dr.Flom from his volunteer private patients. Several years later, Daugman received a method patent for the
algorithm and a crudely constructed prototype proved the concept. The three individuals then founded
"IridianTechnologies,Inc." and assigned the Flom/Safir patent to that entity that was then capitalized by GE Capital,
a branch of "GE"(General Electric) and other investors.

"Iridian" then licensed several corporations to the exclusive Daugman algorithm under the protection of the
Flom/Safir broad umbrella patent listed above; thus, preventing other algorithms from competing. Upon expiration
of the Flom/Safir patent in 2008 other algorithms were patented and several were found to be superior to
Daugman's and are now being funded by U.S. Government agencies.

A facial recognition system is a computer application for automatically identifying or
verifying a person from a digital image or a video frame from a video source. One of the
ways to do this is by comparing selected facial features from the image and a facial

It is typically used in security systems and can be compared to other biometrics such as
fingerprint or eye iris recognition systems.[1]

Facial recognition

DNA profiling

DNA profiling (also called DNA testing, DNA typing, or genetic fingerprinting) is a
technique employed by forensic scientists to assist in the identification of individuals by
their respective DNA profiles. DNA profiles are encrypted sets of numbers that reflect a
person's DNA makeup, which can also be used as the person's identifier. DNA profiling
should not be confused with full genome sequencing.[1] It is used in, for example,
parental testing and criminal investigation.

Although 99.9% of human DNA sequences are the same in every person, enough of the
DNA is different to distinguish one individual from another, unless they are monozygotic
twins.[2] DNA profiling uses repetitive ("repeat") sequences that are highly variable,[2]
called variable number tandem repeats (VNTRs), particularly short tandem repeats
(STRs). VNTR loci are very similar between closely related humans, but so variable that
unrelated individuals are extremely unlikely to have the same VNTRs.

The DNA profiling technique was first reported in 1984[3] by Sir Alec Jeffreys at the
University of Leicester in England,[4] and is now the basis of several national DNA
databases. Dr. Jeffreys's genetic fingerprinting was made commercially available in 1987,
when a chemical company, Imperial Chemical Industries (ICI), started a blood
centre in England.[5]

Speaker recognition

Speaker recognition[1] is the identification of the person who is speaking by
characteristics of their voices (voice biometrics), also called voice

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, and "voice recognition" can be used for both. In addition, there is a difference
between the act of authentication (commonly referred to as speaker verification or
speaker authentication) and identification. Finally, there is a difference between speaker
recognition (recognizing who is speaking) and speaker diarisation (recognizing when the
same speaker is speaking). Recognizing the speaker can simplify the task of translating
speech in systems that have been trained on specific person's voices or it can be used
to authenticate or verify the identity of a speaker as part of a security process.

Speaker recognition has a history dating back some four decades and 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). Speaker verification has earned
speaker recognition its classification as a "behavioral biometric".

Signature recognition

Signature recognition is a behavioural biometric. It can be operated in two different ways:

Static: In this mode, users write their signature on paper, digitize it through an optical
scanner or a camera, and the biometric system recognizes the signature analyzing its
shape. This group is also known as “off

Dynamic: In this mode, users write their signature in a digitizing tablet, which acquires
the signature in real time. Another possibility is the acquisition by means of stylus
operated PDAs. Dynamic recognition is also known as “on
line”. Dynamic information
usually consists of the following information:

spatial coordinate x(t)

spatial coordinate y(t)

pressure p(t)

azimuth az(t)

inclination in(t)

The state
art in signature recognition can be found in the last major international

The most popular pattern recognition techniques applied for signature recognition are
Dynamic Time Warping (DTW), Hidden Markov Models (HMM) and Vector Quantization
(VQ). Combinations of different techniques also exist.[2]