Application Of Facial Recognition To Authorize Credit Card Payments

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Abstract- Facial recognition to authorize credit card payment is a new technology in paying by using card. To prevent the unauthorized user for any amount of payment, there is an improvement of technology, provides the idea of facial scanning before the payment. This system will focus on the authorized user for the payment they made and made an answer for the system to complete the payment. It is highly recommended facial payment will be implemented to reduce unauthorized credit card user to made amount of payment.

Keywords: facial recognition, safety, authorized user, official permission, approval.

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I. Introduction

Credit card fraud is where unfamiliar individuals have in their possession victims credit card number illegally by the way of stolen credit cards, phishing method through emails, or skimming method where attackers use a machine that duplicates the date from the original card to make another illegal copy of the card. In this case, identify theft is having access or misusing someone’s personal information for their personal gain.

From previous article, over the past few years, computers nowadays have become incredibly good at facial recognizing and the technology is expanding quickly in China but in our country (Malaysia). Face recognizing might transform everything from authorize the way people interact every day with stores, restaurants, banks and any other payment stores. As we know that this technology is already being used in several popular apps such as Instagram, Snapchat, Wechat and more. In the apps of Wechat, it is possible to transfer money through Alipay which is a mobile payment app used by more than 120 million people in China according to https://www.technologyreview.com, using only face as credentials. Meanwhile, Didi, a China’s dominant ride-hailing company, uses the facial recognizing software to let passengers confirm that the person behind the wheel is an authorized driver.

Based on the above worldwide statistics shows that credit card fraud is too serious of losing in Billions dollar. Meanwhile, facial recognizing technology is to determine the payment that user made is authorized to prevent the card fraud from the unauthorized user. The aim is to automate and make a system that provides a reliable and efficient mode of online transaction process. The technology has the authority to authenticate or restrict a user. It should be flexible enough so that people can easily use it without any hesitation. Face recognizing is one of the few biometric methods that process the merits of both high accuracy and low intrusiveness. It has the accuracy of physiological approach without being intrusive. The below diagram showing the overall system and division of modules:

The above diagram shows that the user got authenticated by the technology during its transaction comparing the features of the user image to the features stored in administrator module by the administrator. MVC as known as Model View Controller, is a software design pattern for developing web application. MVC is a popular as it isolates the application logic from the user interface layer and supports separation of concerns.

Key factors of card fraud:

  1. The total value of fraudulent transactions conducted using issued within SEPA and acquired worldwide amounted to €1.8 billion in year 2016.
  2. On 19 October, the country woke up to a banking nightmare. The state Bank of India (SBI) blocked 6 lakh debit cards after a reported malware-related breach in a nonSBI ATM network.
  3. The total number of cases of card fraud issued in SEPA amounted to 7.9million in 2011. (This represented a decrease of 6.4% since 2010 and 3.0% since 2007)

The advantages is to automate and make a system that provides a reliable and efficient mode of online transaction process. The system has the authority to authenticate or restrict authorized and unauthorized user. It should be flexible enough so that people can easily use it without any hesitation. The camera plays a crucial role in the working of the system hence the image quality and the performance of the camera in real-time scenario must be tested thoroughly before actual implementation. This method is secure, reliable, and available enough to use. And no need for specialized hardware for installing the system, it can be constructed using camera and computer.

Problem statement

The way of giving users to trust on the system and fully utilise the system in order to archive card fraud. This aims to answer the research objective via the research question in the table below:

  • RQ1 What is the effect after this system is implemented on every stores.
  • RQ2 The cost of implementing the system of every stores.
  • RQ3 How to encourage users to fully utilize this system.
  • RQ4 How to encourage stores which is not using this system for user.

There are 4 research question to determine the value of creations. To narrow down the scope, survey form and details will be published on popular social media application such as Facebook, Instagram, Snapchat, Wechat, Whatsapp, Twitter. Each research question will be answered by each research objective in the table below.

  • RO1 How card user using to the implemented stores?
  • RO2 Knowing the cost of implementing this system.
  • RO3 Attract user with previous problems and case fraud; easy and simple to cope on.
  • RO4 To simplest form of transaction or payment to their purchased item.

Based on the table above, this study is aimed to develop facial payment system that authorized user to make payment in a safety way.

  • RO1: It is just like Touch&Go system, scan it with face and complete the payment.
  • RO2: It is free of charge of cost by simply clicking on the smartphones and download the software.
  • RO3: To attract user with previous cases of card fraud and how easy the system works. Thus, to build trust to the system and fully using it with doubtful.
  • RO4: To allow user to be able to install and download the software and using it with the easiest way.

H1: Every transaction will safely paid

As the system software will recognise authorized user faces or image, it will identify the authorized user to safely make any payment require.

H2: Once the cost of implementing is acceptable

To those who are authorized user will make their oayment with their credit card more safety without any card fraud.

H3: image or face recognition will also help to reduce data input required for the software.

By snapping a picture or image of authorized user, user will be more easy during any payment. This would be more efficient and save more time for authorized user and motivate user to use the software which implemented.

H4: This software system will help people to achieve their safety transactions.

Once with the features provided in the software system, user can usually make more self-easy to every transaction. Moreover, with the technology today, smartphone user can actually use the app whenever they go with simply scanning or snapping their face to make transactions.

II. Methodology

This study will apply mixed method to answer how and why the question from the target audience more effectively and efficiently. The details from the survey will be occur during the data collection. As the table below shows that the data collected via the following methodology as summarized.

  • Research Dimension
  • Explanatory sequential Design
  • Research Methodology
  • Mixed Mode
  • Research Methods
  • Comparative Analysis

Based on the table above, the research dimension will be explained in a sequenced diagram that will explain every step on data collection. To elaborate of the steps of data collection, the sequenced diagram will illustrate as shown in the diagram below.

During the quantitative data collection, a survey will be placed on various popular social media to collect information, the generalise information will be gathered. Even though this approach aims to solve issues by integrating the facial recognition in the process, there is still much more room for improvement. Since implementing a modular approach we can improve different modules until it reaches an acceptable detection, identification and authentication. To finalize and meet the conclusion, all information collected. Moreover, there are 7 respondents for the survey and 2 of them will be in an interview. As the actual data collection, there will be 30 surevey to be complete by all the random people and 50 interviewees to be interviewed.

References

  1. https://www.technologyreview.com/s/603494/10-breakthrough-technologies-2017-paying-with-your-face/
  2. https://www.apps.razak.utm.my
  3. https://www.google.com/search?q=credit+card+fraud+case+chart+malaysia&client=firefox-b-d&hl=en-GB&sxsrf=ACYBGNQOd3C1ZtrdOlnYBm7Ex5BqNqqHuw:1581367325523&source=lnms&tbm=isch&sa=X&ved=2ahUKEwiQycmt7MfnAhXswzgGHbMyDmwQ_AUoAXoECAwQAw&biw=1536&bih=728#imgrc=XpTFbOESj5r0cM
  4. https://economictimes.indiatimes.com/wealth/spend/how-to-avoid-card-fraud/articleshow/55127030.cms?from=mdr
  5. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=17&cad=rja&uact=8&ved=2ahUKEwje0N7A8sfnAhWfzjgGHcL8BuIQFjAQegQICBAB&url=https%3A%2F%2Fwww.ecb.europa.eu%2Fpub%2Fcardfraud%2Fhtml%2Fecb.cardfraudreport201809.en.html&usg=AOvVaw3JDYzpanhCj79ruDfzm2fU
  6. https://www.thestar.com.my/tech/tech-news/2019/09/05/chinese-shoppers-adopt-facial-payments-in-cashless-drive

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