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

In Industrial Internet of Things environments, dependability of complex manufacturing process in which human operators play a key role can be improved by identity recognition/authentication of whoever is involved in the 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 pat- terns captured from edge IoT devices by means of LBP-TOP descriptor, which effectively extract both face’s local features and movement at the fog level of the architecture. A deep feed-forward 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.