Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/14762
Title: Automated POS based on eye pattern identification
Authors: Sammut, Gabriel
Keywords: Biometric identification
Pattern recognition systems
Debit cards
Issue Date: 2016
Abstract: Traditionally card-based payment systems have improved over the past decade, especially with EMVco standards and the widespread use of chip integrated cards. However, fraud and card theft is still an issue, and new ways to breach the integrity of the payment card increases yearly, making it ever important to properly secure this means of payment. The importance that the payment card presents makes it a highly valuable and potential key, yet very dangerous in the wrong hands. With the rise of biometrics and their usage in the fields of security, their use in everyday life has increased substantially. They have been proven multiple times to work where security and user authentication are prime concerns due to their difficulty of replication, tampering or theft. Some justify concerns against usage of biometrics, such as the intrusiveness of the technology or its accuracy. The iris biometric in particular is ideal, due to the speed of its authentication, its non-intrusive nature, the high user acceptance and recognition rates. This study aims to propose a solution and replacement to the commonly-used credit/debit cards by opting for a biometric approach, namely Iris recognition. This approach could enable individuals to retrieve their bank account details by simply scanning their eye, without the burden of having to carry any payment cards which can be targeted for malicious intent. This paper aims to project the iris biometric as a „replacement‟ to the payment card primarily in point of sale (person to person) transactions, however the proof of concept also allows for variations of this approach, such as using the technology as a two step authentication factor, or implementing it for ATM money withdrawals.
Description: B.SC.IT(HONS)
URI: https://www.um.edu.mt/library/oar//handle/123456789/14762
Appears in Collections:Dissertations - FacICT - 2016
Dissertations - FacICTCIS - 2016

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