Selfie banking: is it a reality?

Cook, Steve

doi:10.1016/s0969-4765(17)30056-5

“…The authentication technology using biometrics using fingerprint, iris, voice [31] [32] [33]; face biometry [34] supported by artificial intelligence technology can be used as technical solutions to be explored and adopted for performing a transaction with minimum contact. These solutions can be used for customer on-boarding, such as taking selfie photos [51], and integrated into the government ID verification system for comparing against Government ID. Other possibilities include using an electronic signature for opening bank accounts and mortgage applications [64].…”

Section: Discussionmentioning

confidence: 99%

“…Face recognition is more reliable than fingerprinting biometrics, because it can identify liveness functionality. Furthermore, if face recognition is integrated with other common modalities, such as voice, iris, or fingerprint, this process will further enhance confidence in the transactional process in digital banking [51].…”

Section: Rq1: How Do the Banks Implement The Relevant Technologies Fo...mentioning

confidence: 99%

Intelligent Digital Banking Technology and Architecture

Indriasari

¹

,

Prabowo

²

,

Gaol

³

et al. 2022

Int. J. Interact. Mob. Technol.

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The digital banking sector has made tremendous progress in user-friendly, efficient, and rapid financial transactions, owing to the exponential growth of ICT technologies. Consequently, many new banking products, services, and business opportunities have emerged. This paper presents a comprehensive analysis of new emerging technologies and architecture that can support the development of intelligent digital banking platforms. The aim of this study is to explore the latest relevant technology to enhance customer experience and minimize human interaction to prevent the spread of COVID-19. The study follows Kitchenham's SLR principles and goes into great depth regarding the process of choosing and analyzing relevant research papers. This research promoted 40 articles that discuss potential emerging technologies, such as artificial intelligence, blockchain, big data, biometrics, and cloud computing. Some key areas of literature were examined, such as technological trends, potential features, and technical solutions. The articles selected were based on specific criteria and used high-quality databases, including IEEE Xplore, Elsevier, SpringerLink, ACM, and AIS. This study considered recent papers published between 2015 and 2021. The mapping study presents digital banking technology and proposes an integrated architecture for developing intelligent digital banking platforms.

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“…The recent acceptance in the social context of taking selfies in public is playing an essential role in the acceptability of face verification on a smartphone, leading to the socially acceptable possibility of selfie authentication or selfie banking. In work presented by Cook [6] in 2017, the authors underline that an increasing number of users are checking their bank accounts using their mobile devices, and they are willing to use face verification as a biometric over other modalities such as fingerprint, as they considered it more reliable and, through liveness detection, more secure.…”

Section: U N C O R R E C T E D P R O O Fmentioning

confidence: 99%

Selfie Biometrics

Rattani¹,

Derakhshani²,

Ross³

2019

Advances in Computer Vision and Pattern Recognition

25

0

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Taking a "selfie" using a mobile device has become a natural gesture in everyday life. This simple action has many similarities to face authentication on a smartphone: positioning the camera, adjusting the pose, choosing the right background and looking for the best lighting conditions. In the context of face authentication, most of the standardised processes and best practice for image quality is mainly focused on passport images and only recently has the attention of research moved to mobile devices. There is a lack of an agile methodology that adapts the characteristics of facial images taken on smartphone cameras in an unconstrained environment. The main objective of our study is to improve the performances of facial verification systems when implemented on smartphones. We asked 53 participants to take a minimum of 150 "selfies" suitable for biometric verification on an Android smartphone. Images were considered from constrained and unconstrained environments, where users took images both in indoor and outdoor locations, simulating real-life scenarios. We subsequently calculated the quality metrics for each image. To understand how each quality metric affected the authentication outcome, we obtained biometric scores from the comparison of each image to a range of images. Our results describe how each quality metric is affected by the environment variations and user pose using the biometric scores obtained. Our study is a contribution to improve the performance and the adaptability of face verification systems to any environmental conditions, applications and devices.

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Selfies for Mobile Biometrics: Sample Quality in Unconstrained Environments

Lunerti

¹

,

Guest

²

,

Blanco-Gonzalo

³

et al. 2019

Selfie Biometrics

0

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Taking a "selfie" using a mobile device has become a natural gesture in everyday life. This simple action has many similarities to face authentication on a smartphone: positioning the camera, adjusting the pose, choosing the right background and looking for the best lighting conditions. In the context of face authentication, most of the standardised processes and best practice for image quality is mainly focused on passport images and only recently has the attention of research moved to mobile devices. There is a lack of an agile methodology that adapts the characteristics of facial images taken on smartphone cameras in an unconstrained environment. The main objective of our study is to improve the performances of facial verification systems when implemented on smartphones. We asked 53 participants to take a minimum of 150 "selfies" suitable for biometric verification on an Android smartphone. Images were considered from constrained and unconstrained environments, where users took images both in indoor and outdoor locations, simulating real-life scenarios. We subsequently calculated the quality metrics for each image. To understand how each quality metric affected the authentication outcome, we obtained biometric scores from the comparison of each image to a range of images. Our results describe how each quality metric is affected by the environment variations and user pose using the biometric scores obtained. Our study is a contribution to improve the performance and the adaptability of face verification systems to any environmental conditions, applications and devices.

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Selfie banking: is it a reality?

Cited by 15 publications

References 0 publications

Intelligent Digital Banking Technology and Architecture

Intelligent Digital Banking Technology and Architecture

Selfie Biometrics

Selfies for Mobile Biometrics: Sample Quality in Unconstrained Environments

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