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Head pose estimation (HPE) represents a topic central to many relevant research fields and characterized by a wide application range. In particular, HPE performed using a singular RGB frame is particular suitable to be applied at best-frame-selection problems. This explains a growing interest witnessed by a large number of contributions, most of which exploit deep learning architectures and require extensive training sessions to achieve accuracy and robustness in estimating head rotations on three axes. However, methods alternative to machine learning approaches could be capable of similar if not better performance. To this regard, we present FASHE, an approach based on partitioned iterated function systems (PIFS) to represent auto-similarities within face image through a contractive affine function transforming the domain blocks extracted only once by a single frontal reference image, in a good approximation of the range blocks which the target image has been partitioned into. Pose estimation is achieved by finding the closest match between fractal code of target image and a reference array by means of Hamming distance. The results of experiments conducted exceed the state of the art on both Biwi and Ponting’04 datasets as well as approaching those of the best performing methods on the challenging AFLW2000 database. In addition, the applications to GOTCHA Video Dataset demonstrate that FASHE successfully operates in-the-wild. [ABSTRACT FROM AUTHOR]

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]

Thanks to the advances of voltage regulator (VR) technologies and haptic systems, virtual simulators are increasingly becoming a viable alternative to physical simulators in medicine and surgery, though many challenges still remain. In this study, a pervasive visual-haptic framework aimed to the training of obstetricians and midwives to vaginal delivery is described. The haptic feedback is provided by means of two hand-based haptic devices able to reproduce force-feedbacks on fingers and arms, thus enabling a much more realistic manipulation respect to stilus-based solutions. The interactive simulation is not solely driven by an approximated model of complex forces and physical constraints but, instead, is approached by a formal modeling of the whole labor and of the assistance/intervention procedures performed by means of a timed automata network and applied to a parametrical 3-D model of the anatomy, able to mimic a wide range of configurations. This novel methodology is able to represent not only the sequence of the main events associated to either a spontaneous or to an operative childbirth process, but also to help in validating the manual intervention as the actions performed by the user during the simulation are evaluated according to established medical guidelines. A discussion on the first results as well as on the challenges still unaddressed is included. [ABSTRACT FROM AUTHOR]