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Head pose estimation (HPE) notoriously represents a crucial task for many computer vision applications in robotics, biometry and video surveillance. While, in general, HPE can be performed on both still images and frames extracted from live video or captured footage, its functional approach and the related processing pipeline may have a significant impact on suitability to different application contexts. This implies that, for any real‐time application in which HPE is required, this information, namely the angular value of yaw, pitch and roll axes, should be provided in real‐time as well. Since, so far, the primary aim in HPE research has been on improving estimation accuracy, there are only a few works reporting the computing time of the proposed HPE method and even less explicitly addressing it. The present work stems from a previous Partitioned Iterated Function Systems‐based approach providing state‐of‐the‐art accuracy with high computing cost, and improve it by means of two regression models, namely Gradient Boosting Regressor and Extreme Gradient Boosting Regressor, achieving much faster response and an even lower mean absolute error on the yaw and roll axis, as shown by experiments conducted on the BIWI and AFLW2000 datasets. [ABSTRACT FROM AUTHOR]

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]

The design of a virtual reality (VR) cultural application is aimed at supporting the steps of the learning process-like concrete experimentation, reflection and abstraction—which are generally difficult to induce when looking at ruins and artifacts that bring back to the past. With the use of virtual technologies (e.g., holographic surfaces, head-mounted displays, motion—cation sensors) those steps are surely supported thanks to the immersiveness and natural interaction granted by such devices. VR can indeed help to symbolically recreate the context of life of cultural objects, presenting them in their original place of belonging, while they were used for example, increasing awareness and understanding of history. The ArkaeVision VR application takes advantages of storytelling and user experience design to tell the story of artifacts and sites of an important cultural heritage site of Italy, Paestum, creating a dramaturgy around them and relying upon historical and artistic content revised by experts. Visitors will virtually travel into the temple dedicated to Hera II of Paestum, in the first half of the fifth century BC, wearing an immersive viewer–HTC Vive; here, they will interact with the priestess Ariadne, a digital actor, who will guide them on a virtual tour presenting the beliefs, the values and habits of an ancient population of the Magna Graecia city. In the immersive VR application, the memory is indeed influenced by the visitors' ability to proceed with the exploratory activity. Two evaluation sessions were planned and conducted to understand the effectiveness of the immersive experience, usability of the virtual device and the learnability of the digital storytelling. Results revealed that certainly the realism of the virtual reconstructions, the atmosphere and the "sense of the past" that pervades the whole VR cultural experience, characterize the positive feedback of visitors, their emotional engagement and their interest to proceed with the exploration. [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]

The paper presents a face recognition system based on 3D features with the aim of verifying the identity of subjects accessing a controlled Ambient Intelligence Environment and customizing all the services accordingly. The proposed approach relies on stereoscopic face acquisition and 3D mesh reconstruction avoiding non-automated and expensive 3D scanners, unsuitable for real time applications in general. A bidimensional feature descriptor is extracted from each 3D mesh. It consists in a color image transferring face's 3D features in a 2D space. An automatic weighting mask of each authorized person improves the robustness of recognition in presence of diverse facial expressions and beard. The experiments conducted show high average recognition rate and a measurable effectiveness of both flesh mask and expression weighting mask. [ABSTRACT FROM AUTHOR]