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Authentication Tools |
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Fingerprint Recognition
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Face Recognition
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Iris Recognition
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Hand Scan
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Voice Recognition
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Fingerprint recognition
Fingerprint biometrics is probably the most common form of biometrics available
today. Fingerprints, when scanned electronically, provide greater details and
hence higher level of accuracy can be achieved over manual systems. Studies have
also found that using fingerprints as an identification source is the least
intrusive of all biometric technique.
Users experience fewer errors in matching when they use fingerprints as against
many other biometric methods. Several companies have produced capture units
smaller than a deck of cards. Finger-scan technology is thus the most prominent
biometric authentication technology, used by millions of people worldwide. Used
for decades in forensic applications, finger-scan technology is steadily gaining
acceptance in fields as varied as physical access, network security, service
access, e-commerce and retail.
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Face recognition:
Facial scan technology is an increasingly prominent biometric authentication
technology, one well suited for a number of applications in which other
biometric technologies are simply unusable.
Making use of distinctive features or characteristics of the human face, often
irrespective of facial hair or glasses, facial scan is deployed in fields as
varied as physical access, surveillance, PC access, and ATM access.
Authentication process involves the user entering some identifying information
such as a login name or pin, having a snapshot taken in front of the camera and
then being verified. It also has the ability to track moving faces. There are
four primary methods used to identify and verify users. They include eigenfaces,
feature analysis, neural network and automatic face processing. Eigenfaces,
which means roughly "one’s own face", is a MIT patented technology, which
utilizes two dimensional grayscale images representing distinctive
characteristics of a facial image. Neural Network analyses features from both
images, the enrollment and verification image and determines if there is a match
using an algorithm. Automatic Facial Processing (AFP) uses distances and
distance ratios between certain features of the face, namely eyes, end of nose
and corner of mouth. It is not as robust as Eigenfaces, but would be more
effective in a dimly lit situation.
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Iris recognition
Iris biometrics is exceptionally accurate, especially in environments where the
fingerprints are worn out due to hard manual labor. Iris technology is
relatively more expensive to use and does take-up slightly more time for the
enrollment and authentication process. Iris scanners are typically multi-purpose
and incorporate regular video capabilities with the scanner. Iris biometric
devices are more accurate than fingerprint because an iris has more
characteristics to identify and match than those found on the finger. The error
rate for the typical iris scanner is about one in two million attempts, which
further demonstrates the reliability of this technology.There are several industries, which are interested in this type of technology,
particularly banking & Finance. Banks are incorporating Iris Scanning systems
into their ATMs. Some prisons are also using this technology today to identify
inmates and guards.
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Hand Scan
Hand-scan is a relatively accurate technology, but does not draw as rich a data
set as finger, face, or iris. Hand-scan does not perform one-to-many
identification, as similarities between hands are not uncommon. The submission
of the biometric is straightforward, and with proper training can be done with
little misplacement. The template size of a hand scan is up to 9 bytes which is
extremely small compared to most other biometric technologies. By contrast,
finger scan biometric requires 250-1000 bytes and voice scan biometric commonly
requires 1500-3000 bytes. This facilitates storage of a large number of
templates in a standalone device. It also facilitates card-based storage, as
even magstripe cards have ample room for 9 byte samples.
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Voice recognition
Voice recognition is "the technology by which sounds, words or phrases spoken by
humans are converted into electrical signals, and these signals are transformed
into codsing patterns to which meaning has been assigned" The most common
approaches to voice recognition can be divided into two classes: "template
matching" and "feature analysis". Template matching in voice recognition is the
simplest technique and has the highest accuracy when used properly, but it also
suffers from the most limitations. As with any approach to voice recognition,
the first step is for the user to speak a word or phrase into a microphone, the
electrical signal from the microphone is digitized by an "analog-to-digital
(A/D) converter", and is stored in memory. To determine the "meaning" of this
voice input, the computer attempts to match the input with a digitized voice
sample, or template, that has a known meaning.
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