TY - GEN
T1 - A Model for Biometric Selection in Public Services Sector
AU - Maeko, Mapula Elisa
AU - van der Haar, Dustin
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The need to authenticate people using their biometric attributes and tighten information security in organisations significantly increased over the years and public services are no exception. Selecting suitable, robust, relevant and beneficial multimodal biometric attributes in public services environment for person authentication and access control is essential. The major challenge is deploying the wrong multimodal biometric technology in the organisation, which results in failed system deployment. Artificial intelligence (AI) has the potential to significantly drive the adoption and deployment of multimodal biometric authentication in public services. The study recommends a multimodal biometrics selection model for authentication to prevent fraudulent and invalid documents for identification. This study focuses on the human factor elements of public awareness, acceptance, perception and usability relevant to multimodal biometric deployment success. The formalised model proposed in the study could be of value to public services that need to deploy multimodal biometric authentication technologies, thereby minimising future failed deployments.
AB - The need to authenticate people using their biometric attributes and tighten information security in organisations significantly increased over the years and public services are no exception. Selecting suitable, robust, relevant and beneficial multimodal biometric attributes in public services environment for person authentication and access control is essential. The major challenge is deploying the wrong multimodal biometric technology in the organisation, which results in failed system deployment. Artificial intelligence (AI) has the potential to significantly drive the adoption and deployment of multimodal biometric authentication in public services. The study recommends a multimodal biometrics selection model for authentication to prevent fraudulent and invalid documents for identification. This study focuses on the human factor elements of public awareness, acceptance, perception and usability relevant to multimodal biometric deployment success. The formalised model proposed in the study could be of value to public services that need to deploy multimodal biometric authentication technologies, thereby minimising future failed deployments.
KW - Acceptance
KW - Artificial intelligence
KW - Deployment
KW - Multimodal biometrics
KW - Usability
UR - http://www.scopus.com/inward/record.url?scp=85144200624&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-22321-1_22
DO - 10.1007/978-3-031-22321-1_22
M3 - Conference contribution
AN - SCOPUS:85144200624
SN - 9783031223204
T3 - Communications in Computer and Information Science
SP - 323
EP - 334
BT - Artificial Intelligence Research - Third Southern African Conference, SACAIR 2022, Proceedings
A2 - Pillay, Anban
A2 - Jembere, Edgar
A2 - Gerber, Aurona
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd Southern African Conference on Artificial Intelligence Research, SACAIR 2022
Y2 - 5 December 2022 through 9 December 2022
ER -