Automatic Access Control Based on Face and Hand Biometrics in A Non-Cooperative Context

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Abstract

Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the
users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction
procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users.
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Detaljer

Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the
users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction
procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users.
OriginalsprogEngelsk
TitelProceedings - 2018 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2018 : Cross-Domain Biometric Recognition Workshop
Antal sider9
ForlagIEEE
Publikationsdato24 apr. 2018
Sider28-36
ISBN (Elektronisk)9781538651889
DOI
StatusUdgivet - 24 apr. 2018
PublikationsartForskning
Peer reviewJa
Begivenhed 2018 IEEE Winter Applications of Computer Vision Workshops (WACVW) - Lake Tahoe, USA
Varighed: 15 mar. 201815 mar. 2018

Konference

Konference 2018 IEEE Winter Applications of Computer Vision Workshops (WACVW)
LandUSA
ByLake Tahoe
Periode15/03/201815/03/2018
ID: 268847758