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Selfies for mobile biometrics: sample quality in unconstrained environments

Selfies for mobile biometrics: sample quality in unconstrained environments
Selfies for mobile biometrics: sample quality in unconstrained environments
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.
2191-6586
145-167
Springer Cham
Lunerti, Chiara
12ff8320-25c9-4499-9773-d5f629d87d21
Guest, Richard
93533dbd-b101-491b-83cc-39ccfdc18165
Blanco-Gonzalo, Ramon
d41d04ec-86ac-4147-ac7d-d202a9331025
Sanchez-Reillo, Raul
ca38245f-262c-4e86-8ee9-c7521e021db7
Rattani, Ajita
Derakhshani, Reza
Ross, Arun
Lunerti, Chiara
12ff8320-25c9-4499-9773-d5f629d87d21
Guest, Richard
93533dbd-b101-491b-83cc-39ccfdc18165
Blanco-Gonzalo, Ramon
d41d04ec-86ac-4147-ac7d-d202a9331025
Sanchez-Reillo, Raul
ca38245f-262c-4e86-8ee9-c7521e021db7
Rattani, Ajita
Derakhshani, Reza
Ross, Arun

Lunerti, Chiara, Guest, Richard, Blanco-Gonzalo, Ramon and Sanchez-Reillo, Raul (2019) Selfies for mobile biometrics: sample quality in unconstrained environments. In, Rattani, Ajita, Derakhshani, Reza and Ross, Arun (eds.) Selfie Biometrics: Advances and Challenges. (Advances in Computer Vision and Pattern Recognition) Springer Cham, pp. 145-167. (doi:10.1007/978-3-030-26972-2_7).

Record type: Book Section

Abstract

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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More information

e-pub ahead of print date: 21 September 2019
Published date: 2 October 2019

Identifiers

Local EPrints ID: 489706
URI: http://eprints.soton.ac.uk/id/eprint/489706
ISSN: 2191-6586
PURE UUID: aca00555-2ffc-4182-b370-d6145d8d8467
ORCID for Richard Guest: ORCID iD orcid.org/0000-0001-7535-7336

Catalogue record

Date deposited: 30 Apr 2024 16:57
Last modified: 01 May 2024 02:10

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Contributors

Author: Chiara Lunerti
Author: Richard Guest ORCID iD
Author: Ramon Blanco-Gonzalo
Author: Raul Sanchez-Reillo
Editor: Ajita Rattani
Editor: Reza Derakhshani
Editor: Arun Ross

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