NECESSITIES FOR BIOMETRIC SECURITY SYSTEM
In
computer technology, biometrics relates to identity-confirmation and security
techniques that rely on measurable, individual biological characteristics. For
example, fingerprints, palmprints, or voice patterns might be used to enable
access to a computer, to a room, or to an electronic commerce account. In
general, there are three levels of computer security schemes. Level 1 relies on
something a person carries, such as an ID badge with a photograph or a computer
cardkey. Level 2 relies on something a person knows, such as a password or a
code number. Level 3, the highest level, relies on something that is a part of
a person’s biological makeup or behavior, such as a fingerprint, a facial
image, or a signature [Teoh et al.,2006]. Biometric systems are commonly used
to control access to physical assets (laboratories, buildings, cash from ATMs,
etc.) or logical information (personal computer accounts, secure electronic
documents, etc). Biometric systems can also be used to determine whether or not
a person is already in a database, such as for social service or national ID
applications. [Jain, Hong, and Pankanti, 2000]
Measurement
of physical features such as height, eye colour, scars, etc, as a method of
personal identity is known to date back to the ancient Egyptians.
Archaeological evidence of fingerprints being used to at least associate a
person with some event or transaction is also said to date back to ancient
China, Babylonia and Assyria. But it was not until the end of the 19th century
that the study of biometrics entered the realm of crime detection [Rattani,
2010]. Alphonse Bertillon, a French police clerk and anthropologist, pioneered
a method of recording multiple body (anthropometric) measurements for criminal
identification purposes. Known as ‘Bertillonage’ it was adopted by many police
authorities worldwide during the 1890s, but soon became obsolete once it was
recognized that people could indeed share the same physical measurements [Jain
et al., 1999]. Meanwhile, the quest for a physical identifier that was unique
to each individual gained significant ground when British anthropologist, Sir
Francis Galton, worked on the principle that fingerprints were permanent throughout
life, and that no two people had identical fingerprints. Galton calculated the
odds of prints from two people being identical to be 1 in 64 billion and also
identified characteristics – known as ‘minutiae’ – that are still used today to
demonstrate that two impressions made by the same finger match. Minutiae are
points of interest formed by the endings or forking of the friction skin ridges
on each finger and are defined as one of the following:
•
Ridge ending – the point at which a ridge terminates
•
Bifurcation – the point at which a single ridge splits into two ridges
Arrangement
of all the minutiae in terms of their location, orientation of ridge flow and
type (i.e. ridge ending or bifurcation) that make an individual’s fingerprints
unique. The flow of the friction skin ridges also form the patterns – the
whorl, arch and loop of each finger – that were identified by Galton. Galton’s
patterns provided the basis of the first fingerprint file established in 1891
by Juan Vucetich, an Argentine police officer, who became the first to use a
bloody fingerprint to prove the identity of a murderer during a criminal
investigation. In 1897, Sir Edward Henry, a British police officer serving as
Inspector General of the Bengal Police in India, also developed an interest in
the use of fingerprints for identifying criminals, even though the Bengal
Police was at that time using Bertillonage. Based on Galton’s observations,
Henry and colleagues established a modified classification system allowing
fingerprints captured on paper forms using an ink pad to be classified, filed
and referenced for comparison against thousands of others. By 1901, Henry’s
fingerprinting system had been adopted in the UK by Scotland Yard and its use
then spread through most of the world (the exception being South America, where
the Vucetich system was used) to become a standard method of identity detection
and verification in criminal investigations. [Rattani, 2010]
As
reported by [Kent, 2005], advent of computers and digital technology in the 1970s,
gave fingerprinting took on a new dimension. As a result, the UK’s fingerprint
service now records 120,000 sets of fingerprints each year – a volume of
records that was simply untenable before computerization.
Within
a century, biometrics had evolved from tape measure, ink and pad techniques
requiring vast manual filing and archiving resources, to an automated biometric
digital scanning process using computerized storage, automated search and
find/match techniques, plus extensive archiving and access systems with
worldwide links. Such technology now provides for the capture and processing of
biometrics information and has transformed fingerprinting techniques and
procedures. [Dabbah et al. 2007] Explained that in the mid-1960s, the Royal
Canadian Mounted Police (RCMP) adopted an automated video tape-based filing
system allowing identification officers to make fingerprint comparisons
on-screen. A similar ‘Video file System’ was installed at New Scotland Yard in
1977. Around the same time, the USA’s Federal Bureau of Investigation (FBI) was
working with industry to build the first automated fingerprint card reader,
which was implemented in 1974. Over the next five years, the FBI and other
organizations in Canada, Japan and the UK, developed further core technologies
including fingerprint matching hardware, plus automated classification software
and hardware. By the early 1980s, this culminated in the automatic fingerprint
identification system (AFIS), which allowed the automatic matching of one or
many unknown fingerprints against an electronic database of known prints;
another major forward step in the world of crime detection and international
security. Such systems have since reduced the manual capture, store, search and
match processes for fingerprints from weeks and months, to hours and minutes,
and have led to AFIS being deployed by law enforcement agencies in Europe and
worldwide. [Pfleeger et al., 2007]
Whenever
biometric identification is discussed, people always want to know about its
implications on personal privacy. If a biometric system is used, will the
government, or some other group, be able to get personal information about the
users? Biometric measures themselves contain no personal information. Hand
shape, fingerprints or eye scans do not reveal name, age, race, gender, and
health or immigration status. Although voice patterns can give a good
estimation of gender, no other biometric identification technology currently
used reveals anything about the person being measured. More common identification
methods, such as a driver’s license, reveal name, address, age, gender, vision
impairment, height and even weight! Driver’s licenses, however, may be easier
to steal or counterfeit than biometric measures [Ratha et al., 2000].
Biometric
measures can be used in place of a name, Social Security number or other form
of identification to secure anonymous transactions. Walt Disney World sells
season passes to buyers anonymously, then uses finger geometry to verify that
the passes are not being transferred. Use of iris or fingerprint recognition
for anonymous health care screening has also been proposed. A patient would use
an anonymous biometric measure, not a name or Social Security number, when
registering at a clinic. All records held at the clinic for that patient would
be identified, linked and retrieved only by the measure. No one at the clinic,
not even the doctors, would know the patient’s “real” (publicly recognized)
identity [Derakhshani et al., 2003]. The real fear is that biometric measures will
link people to personal data, or allow movements to be tracked. After all,
credit card and phone records can be used in court to establish a person’s
activities and movements. There are several important points to be made on this
issue [Cole, 2001].
Databases
of biometric images, and the numerical models or templates derived from them,
are often encrypted with the intention of inhibiting their compromise in bulk.
But compromise of individual measures cannot always be prevented by protecting
databases and transmission channels because biometric measures, although
privately owned, are sometimes publicly observable (e.g. a photo of a person’s
face can be taken with a camera or downloaded from a web page). In general,
biometric measures are not secret, even if it might be quite complicated to
acquire usable copies (e.g. a retinal map) without the cooperation of the
owner. When used for security, biometric characteristics are more like public
keys than private keys. Unlike public keys, however, biometric measures cannot
be revoked if stolen or mimicked. The industry is currently working on methods
for “live-ness testing” and revocation, hoping to ameliorate these problems
[Beaven, 2001].
With
increase in the use of information technology and need to protect data, we have
multiple accounts and passwords. We cannot remember so many passwords, so we
end up using things we know to create them e.g. birthdays, wife/girlfriends
name, dog, cat. e.t.c. It is easy to crack passwords, because most of our
passwords are weak! If we create strong passwords (it might be meaningless to
us) we will forget them! And there is no way to remember such multiple
passwords, in the financial sectors, so many fraudulent transactions have been
noticed, likewise so many life and properties have been tampered with by
unauthorized personnel. Due to this fact, biometric security system is highly
essential.
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