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In the last decade, research in biometrics has been focused on augmenting the algorithmic performance to address a growing range of applications, not limited to person authentication/recognition. The concept of context awareness emerged as a possible key-factor for both performance optimization and operational adaptation of the capture, extraction, matching and decision stages. This may be particularly effective for multi-biometrics systems. The knowledge of the context in which a task is being performed, may provide useful information to the system in several manners. For example, it may allow to adapt to a specific environmental condition, such as shadow or light exposure. On the other hand, it may be possible to select the best available algorithm, among a given set to address the task at hand, which best performs within the given context. This paper aims to provide an overall vision of the main contributions available so far in the field of context-aware biometric systems and methods. The survey is not confined to a particular biometric modality or processing stage, but rather spans the state of the art of several biometric modalities and approaches. A taxonomy of context-aware biometric systems and methods is also proposed, along with a comparison of their features, aims and performances. The analysis will be complemented with a critical discussion about the state of the art also suggesting some future application scenarios. [ABSTRACT FROM AUTHOR]

An exponential growth of the number of plastic surgery treatments specific to face (from the minimally-invasive ones to the real surgical procedures) has characterized the last two decades and it seems likely that this phenomenon, that has social and cultural meanings and implications, could spread even further in the next years as the average cost of these treatments is lowering and the wish for “beautification” is becoming part of the global esthetics sense. For these reasons, face recognition as an established research topic has a new major challenge: delivering methods capable of high recognition accuracy even in case probe and gallery differ by a surgical alteration of face shape. To this aim is of fundamental importance understanding the range and the extent of the modification produced by the various types of treatments or by a combination of them. We present a survey of the state of the art on this topic, starting by an analysis of the diffusion of the facial plastic surgery and describing the key aspects of each of the most statistically relevant treatments available, resumed by a synthetic table. The paper includes a brief description of all the approaches proposed in the field so far to the best of authors' knowledge and a comparison of the performance reported by the existing methods when applied to the most referenced plastic surgery face dataset to date. A critical discussion of the results achieved so far and an insight about the challenges that still have to be addressed concludes this work. [ABSTRACT FROM AUTHOR]

Abstract: Dependability of a system is commonly referred to its reliability, its availability and its maintenability (RAM), but when this concept is applied to user interfaces there is no common agreement on what aspects of user–system interaction are related to a satisfactory RAM level for the whole system. In particular, when dealing with haptic systems, interface dependability may become a crucial issue in medical and in military domains when life-critical systems are to be manipulated or where costly remote control operations are to be performed, like in industrial processes control or in aerospace/automotive engineering and manufacturing. This paper discusses the role of dependability in haptic user interfaces, aiming to the definition of a framework for the assessment of the usability and dependability properties of haptic systems and their possible correlations. The research is based on the analysis of a visual–haptic-based simulator targeted to maintenance activity training for aerospace industry which is taken as a case study. As a result, we propose a novel framework able to collect and then process relevant interaction data during the execution of haptic tasks, enabling to analyze dependability vs. usability correlations. [Copyright &y& Elsevier]

• We propose an approach to compensate the visual defects caused by metamorphopsia • Our approach enables interactive measurement of distortion in user's visual field • We compensate the warped visual field through a real-time processing of video streams • We conducted an experiment on 17 patients affected by metamorphopsia • The results show the proposed system is able to reduce visual field distortion Advances in Augmented Reality technologies and, particularly, the availability of video see-through enabled head mounted displays (HMD), are allowing to devise new strategies to help individuals with visual impairments in daily life. In this work, an approach is proposed to compensate a serious visual impairment, known as metamorphopsia, a vision disorder characterized by deformed images. The goal is to provide patients with a digitally restored visual field, through real-time processing of video see-through streams captured from the HMD. To this regard, we present two contributions, respectively, an interactive discrete modeling of patient's eye-specific vision distortion and a compensation of the latter by means of corresponding real-time counter-distortion of incoming frames. Our approach, indeed, maps each of the video streams acquired by the stereoscopic video see-through cameras aboard the headset on a 2D polygonal mesh which is then counter-warped by moving its vertices based on the previously built distortion model and then displayed, restored, on the HMD's screen. First user evaluations report promising results along with usability issues related to HMD technology. [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]

Iris recognition is one of the most promising fields in biometrics. Notwithstanding this, there are not so many research works addressing it by machine learning techniques. In this survey, we especially focus on recognition, and leave the detection and feature extraction problems in the background. However, the kind of features used to code the iris pattern may significantly influence the complexity of the methods and their performance. In other words, complexity affects learning, and iris patterns require relatively complex feature vectors, even if their size can be optimized. A cross-comparison of these two parameters, feature complexity vs. learning effectiveness, in the context of different learning algorithms, would require an unbiased common benchmark. Moreover, at present it is still very difficult to reproduce techniques and experiments due to the lack of either sufficient implementation details or reliable shared code. [ABSTRACT FROM AUTHOR]

This paper describes a case study concerning the virtual reconstruction and navigation of an archaeological site located in Moregine, near Pompeii as it appeared to archaeologists after the completion of the excavation and including the reconstruction of face and body appearance of a woman that found death there, during the eruption of 79 BC. The main challenges faced in this study concern the visual engine required to delivering possibly unlimited visual quality and the methodology for achieving an ethnically faithful face reconstruction from skull bones. The first objective is tackled by adopting a prerendering based visualization engine, through which environment navigation is achieved following pre-built paths and performing available actions through a context sensitive motion tracking based interface. Secondly, the plausible appearance of the woman's face is reconstructed exploiting an approach based on craniometrical analysis together with a pictorial physiognomic database and content-based image retrieval technology, to the aim of providing more faithful results compared to other methods in literature based solely on statistical data. [ABSTRACT FROM AUTHOR]

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]

This paper describes a case study concerning the virtual reconstruction and navigation of an archaeological site located in Moregine, near Pompeii as it appeared to archaeologists after the completion of the excavation and including the reconstruction of face and body appearance of a woman that found death there, during the eruption of 79 BC. The main challenges faced in this study concern the visual engine required to delivering possibly unlimited visual quality and the methodology for achieving an ethnically faithful face reconstruction from skull bones. The first objective is tackled by adopting a prerendering based visualization engine, through which environment navigation is achieved following pre-built paths and performing available actions through a context sensitive motion tracking based interface. Secondly, the plausible appearance of the woman's face is reconstructed exploiting an approach based on craniometrical analysis together with a pictorial physiognomic database and content-based image retrieval technology, to the aim of providing more faithful results compared to other methods in literature based solely on statistical data. [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]

• The paper presents concrete applications of Visual Question Answering • Domains where VQA has been experimented are presented together with the exploited dataset • The paper suggests some challenging techniques that can be especially suited for specific domains • Some final considerations sketch future work in domain-related VQA Visual Question Answering (VQA) is an extremely stimulating and challenging research area where Computer Vision (CV) and Natural Language Processig (NLP) have recently met. In image captioning and video summarization, the semantic information is completely contained in still images or video dynamics, and it has only to be mined and expressed in a human-consistent way. Differently from this, in VQA semantic information in the same media must be compared with the semantics implied by a question expressed in natural language, doubling the artificial intelligence-related effort. Some recent surveys about VQA approaches have focused on methods underlying either the image-related processing or the verbal-related one, or on the way to consistently fuse the conveyed information. Possible applications are only suggested, and, in fact, most cited works rely on general-purpose datasets that are used to assess the building blocks of a VQA system. This paper rather considers the proposals that focus on real-world applications, possibly using as benchmarks suitable data bound to the application domain. The paper also reports about some recent challenges in VQA research. [ABSTRACT FROM AUTHOR]

Abstract: One of the most interesting research lines about avatars is the design and the implementation of a synthetic behaviour able to drive avatar''s actions according to an adaptive interaction paradigm. This aspect, indeed, is of fundamental importance to many advanced applications involving avatars and humans. This study presents a novel framework exploiting augmented reality to visualize a synthetic 3D virtual guide inside an exhibit or a public gallery, to assist visitors wearing a Head Mounted Display during their visit and providing them with both visual and informative enhancements not available in a standard tour. The Human–avatar interaction is approached through a model based on timed automata to address the conversational issues and to improve the quality of interaction by means of an effective synchronization. A usability study conducted on an application of this research to the “avatar assisted tour” of a roman villa, confirms the efficacy of the approach. [Copyright &y& Elsevier]

Abstract: This work investigates face recognition based on normal maps, and the performance improvement that can be obtained when exploiting it within a multimodal system, where a further independent module processes visible images. We first propose a technique to align two 3D models of a face by means of normal maps, which is very fast while providing an accuracy comparable to well-known and more general techniques such as Iterative Closest Point (ICP). Moreover, we propose a matching criterion based on a technique which exploits difference maps. It does not reduce the dimension of the feature space, but performs a weighted matching between two normal maps. In the second place, we explore the range of performances offered by different linear and nonlinear classifiers, when applied to the normal maps generated from the above aligned models. Such experiments highlight the added value of chromatic information contained in normal maps. We analyse a solid list of classifiers which were selected due to their historical reference value (e.g. Principal Component Analysis) or to their good performances in the bidimensional setting (Linear Discriminant Analysis, Partitioned Iterated Function Systems). Last but not least, we perform experiments to measure how different ways of combining normal maps and visible images can enhance the results obtained by the single recognition systems, given that specific characteristics of the images are taken into account. For these last experiments we only consider the classifier giving the best average results in the preceding ones, namely the PIFS-based one. [Copyright &y& Elsevier]

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 worldwide diffusion of latest generations mobile devices, namely smartphones and tablets, represents the technological premise to a new wave of applications for which reliable owner identification is becoming a key requirement. This crucial task can be approached by means of biometrics (face, iris or fingerprint) by exploiting high resolution imaging sensors typically built-in on this class of devices, possibly resulting in a ubiquitous platform to verify owner identity during any kind of transaction involving the exchange of sensible data. Among the aforementioned biometrics, iris is known for its inherent invariance and accuracy, though only a few works have explored this topic on mobile devices. In this paper a comprehensive method for iris authentication on mobiles by means of spatial histograms is described. The proposed approach has been tested on the MICHE-I iris dataset, featuring subjects captured indoor and outdoor under controlled and uncontrolled conditions by means of built-in cameras aboard three among the most diffused smartphones/tablets on the market. The experimental results collected, provide an interesting insight about the readiness of mobile technology with regard to iris recognition. [ABSTRACT FROM AUTHOR]

Abstract: In the last years, the industrial world has been increasingly adopting computer-aided solutions for design for maintainability and maintenance training tasks with the goal to reduce development costs and to shorten time, and to improve product and service quality. Computer-based training systems created to simulate machine assembly maintenance are normally operated by means of ordinary human–computer interfaces (keyboard, mouse, etc.), but this usually results in systems that are far from the real procedures, and therefore not effective in terms of training. In this study, we show that a better solution may come from the combination of virtual reality techniques and haptic interaction. To this regard, we present the results of a research aimed at testing and evaluating the effectiveness of the haptic feedback for first-person maintenance tasks targeted to the aerospace industry. The proposed system implements an interaction environment in which each of the main maintenance activities can be simulated by the trainee exploiting a hand-based commercial haptic device, operated by means of specific haptic-rendering techniques to provide realistic feedbacks during manipulation. A usability study is included to help assessing the potential of this approach. [Copyright &y& Elsevier]

• Azure Kinect is applicable in home rehabilitation contexts. • Azure Kinect shows tracking capabilities comparable to multi-cameras systems. • Azure Kinect is highly accurate in tracking upper body movements. • Azure Kinect is more accurate than its predecessor. Equipments generally used for entertainment, such as Microsoft Kinect, have been widely used for postural control as well. Such systems—compared to professional motion tracking systems—allow to obtain non-invasive and low-cost tracking. This makes them particularly suitable for the implementation of home rehabilitation systems. Microsoft has recently released a new version of Kinect, namely Azure Kinect DK, that is meant for developers, not consumers, and it has been specifically designed to implement professional applications. The hardware of this new version of the Kinect has been substantially improved as compared with previous versions. However, the accuracy of the Azure Kinect DK has not been evaluated yet in the context of the assessment of postural control as done for its predecessors. We present a study to compare the motion traces of the Azure Kinect DK with those of a Vicon 3D system, typically considered the gold standard for high-accuracy motion tracking. The study involved 26 subjects performing specific functional reach and functional balance exercises. The results clearly indicates that the Azure Kinect DK provides a very accurate tracking of the main joints of the body for all the recording taken during the lateral reach movement. The Root Mean Square Error (RMSE) between the two tracking systems obtained is approximately 0.2 for the lateral and forward exercises while for the balance exercise it is around 0.47 considering the average of the results among all the joints. The angular Mean Absolute Error is approximately in the range 5–15 degrees for all the upper joints and independently on the exercise. The lower body joints show a higher angular error between the two systems. Not surprisingly, it was found that results are much better in correspondence of slow movements. The results achieved that the Azure Kinect DK has an incredibly high potential to be used in applications of home rehabilitation, where the assessment of postural control is a fundamental and crucial activity. [ABSTRACT FROM AUTHOR]