Trustworthy Method for Person Identification in IIoT Environments by Means of Facial Dynamics

Aniello Castiglione,Stefano Ricciardi,Michele Nappi

doi:10.1109/tii.2020.2977774

Abstract

In industrial Internet of Things (IIoT) environments, dependability of a complex manufacturing process in which human operators play a key role can be improved by identity recognition/authentication of whoever is involved in various stages of a production process, according to where and when he/she is supposed to be. To this aim, we propose an approach that exploits the dynamic appearance and the time-dependent local features characterizing the face of an individual during speech utterance with regard to their spatial and temporal components. The proposed method models these dynamic facial patterns captured from edge Internet of Things devices by means of the Local Binary Pattern on Three Orthogonal Planes descriptor, which effectively extract both face's local features and movement at the fog level of the architecture. A deep feedforward network available in the cloud is trained and optimized to match the extracted features to a reference database. The achieved results highlight state-of-the-art performances of the proposed method with regard to robustness and trustworthiness of identification, especially for challenging IIoT scenarios.

Highlights

I N THE past few years, the diffusion of the industrial Internet of Things (IIoT), as a network of a multitude of industrial devices connected by communications technologies [1], has become increasingly more pervasive in many industrial contexts
To the aim of addressing the aforementioned challenges, we propose a highly trustworthy face recognition approach based on dynamic features, which can be acquired, extracted, and matched to a reference gallery, respectively, at the edge, fog, and cloud levels of an IIoT architecture
We presented a method for identifying/authenticating persons operating within a three-level IIoT environment by means of a dynamic biometric signature

Summary

Introduction

I N THE past few years, the diffusion of the industrial Internet of Things (IIoT), as a network of a multitude of industrial devices connected by communications technologies [1], has become increasingly more pervasive in many industrial contexts. This diffusion is supported by the promise of bringing together smart machines, advanced analytics, and people at work to the aim of enabling unprecedented levels of efficiency, productivity, and performance [2].

Methods

Results

Conclusion