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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. [ABSTRACT FROM AUTHOR]

Today, identity verification is required in many common activities, and it is arguably true that most people would like to be authenticated in the easiest and most transparent way, without having to remember a personal identification number. To this regard, this paper presents a multibiometric system based on the observation that the instinctive gesture of responding to a phone call can be used to capture two different biometrics, namely ear and arm gesture, which are complementary due to their, respectively, physical and behavioral nature. We conducted a comprehensive set of experiments aimed at assessing the contribution of each of the two biometrics as well as the advantage in their fusion to the system’s overall performance. Experiments also provide objective measurement of both saliency and correlation of data captured by each sensor involved (accelerometer, gyroscope, and camera) according to various features extraction, features matching, and data-fusion techniques. The reports provide evidences about the potential of the proposed system and method for user authentication “in-the-wild,” whilst its eventual usage for person identification is also investigated. All of the experiments have been carried out on a specifically built, publicly available ear–arm database, including multibiometric captures of more than 100 subjects performed during different sessions, that represents an additional contribution of this paper. [ABSTRACT FROM AUTHOR]