There is an urgent need for improving security in banking region. With the
advent of ATM though banking became a lot easier it even became a lot vulnerable.
The chances of misuse of this much hyped 'insecure' baby product (ATM) are manifold
due to the exponential growth of 'intelligent' criminals day by day. ATM systems
today use no more than an access card and PIN for identity verification. This
situation is unfortunate since tremendous progress has been made in biometric
identification techniques, including finger printing, retina scanning, and facial
recognition. This paper proposes the development of a system that integrates facial
recognition technology into the identity verification process used in ATMs. The
development of such a system would serve to protect consumers and financial institutions
alike from fraud and other breaches of security.
rise of technology in India has brought into force many types of equipment that
aim at more customer satisfaction. ATM is one such machine which made money transactions
easy for customers to bank. The other side of this improvement is the enhancement
of the culprit's probability to get his 'unauthentic' share. Traditionally, security
is handled by requiring the combination of a physical access card and a PIN or
other password in order to access a customer's account. This model invites fraudulent
attempts through stolen cards, badly-chosen or automatically assigned PINs, cards
with little or no encryption schemes, employees with access to non-encrypted customer
account information and other points of failure.
proposes an automatic teller machine security model that would combine a physical
access card, a PIN, and electronic facial recognition. By forcing the ATM to match
a live image of a customer's face with an image stored in a bank database that
is associated with the account number, the damage to be caused by stolen cards
and PINs is effectively neutralized. Only when the PIN matches the account and
the live image and stored image match would a user be considered fully verified.
The main issues faced in developing such a
model are keeping the time elapsed in the verification process to a negligible
amount, allowing for an appropriate level of variation in a customer's face when
compared to the database image, and that credit cards which can be used at ATMs
to withdraw funds are generally issued by institutions that do not have in-person
contact with the customer, and hence no opportunity to acquire a photo.Because
the system would only attempt to match two (and later, a few) discrete images,
searching through a large database of possible matching candidates would be unnecessary.
The process would effectively become an exercise
in pattern matching, which would not require a great deal of time. With appropriate
lighting and robust learning software, slight variations could be accounted for
in most cases. Further, a positive visual match would cause the live image to
be stored in the database so that future transactions would have a broader base
from which to compare if the original account image fails to provide a match -
thereby decreasing false negatives.
When a match is made with the PIN but not the
images, the bank could limit transactions in a manner agreed upon by the customer
when the account was opened, and could store the image of the user for later examination
by bank officials. In regards to bank employees gaining access to customer PINs
for use in fraudulent transactions, this system would likewise reduce that threat
to exposure to the low limit imposed by the bank and agreed to by the customer
on visually unverifiable transactions.
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