Secure personal authentication in fog devices via multimodal rank‐level fusion

Abstract

SummaryIn recent years, Fog and edge computing have gained enormous increases and usage of Internet of Things (IoT) devices in research and development even though it provides challenges such as security and privacy. One way to ensure this is a fusion of multimodal biometrics authentication systems, which offer highly reliable biometric authentication solutions to reduce the failure to enroll rate, increase the degree of freedom, and deter spoof attacks. Since unimodal biometric systems often face significant limitations due to sensitivity to noise, data quality, non‐universality, and illumination variations. In this research, we propose management of access control to ensure the desired level of security, a Rank level fusion integration method. ORL, GT, CASIA, and VGG‐face databases were used for testing and evaluating the implemented system. Thus, using the LDA with SCE based feature selection approach, the average GAR percent for the facial, hearing, and palm vein is 3.25% for the facial and muffs pulse and 8.55% for the head and palm vein result was the overall performance of the biometric system even in the presence of low‐quality data in the Fog environment.