2D and 3D computer vision analysis of gaze, gender and age

Human-Computer Interaction (HCI) has been an active research area for over four decades. Research studies and commercial designs in this area have been largely facilitated by the visual modality which brings diversified functionality and improved usability to HCI interfaces by employing various computer vision techniques. This thesis explores a number of facial cues, such as gender, age and gaze, by performing 2D and 3D based computer vision analysis. The ultimate aim is to create a natural HCI strategy that can fulfil user expectations, augment user satisfaction and enrich user experience by understanding user characteristics and behaviours. To this end, salient features have been extracted and analysed from 2D and 3D face representations; 3D reconstruction algorithms and their compatible real-world imaging systems have been investigated; case study HCI systems have been designed to demonstrate the reliability, robustness, and applicability of the proposed method.More specifically, an unsupervised approach has been proposed to localise eye centres in images and videos accurately and efficiently. This is achieved by utilisation of two types of geometric features and eye models, complemented by an iris radius constraint and a selective oriented gradient filter specifically tailored to this modular scheme. This approach resolves challenges such as interfering facial edges, undesirable illumination conditions, head poses, and the presence of facial accessories and makeup. Tested on 3 publicly available databases (the BioID database, the GI4E database and the extended Yale Face Database b), and a self-collected database, this method outperforms all the methods in comparison and thus proves to be highly accurate and robust. Based on this approach, a gaze gesture recognition algorithm has been designed to increase the interactivity of HCI systems by encoding eye saccades into a communication channel similar to the role of hand gestures. As well as analysing eye/gaze data that represent user behaviours and reveal user intentions, this thesis also investigates the automatic recognition of user demographics such as gender and age. The Fisher Vector encoding algorithm is employed to construct visual vocabularies as salient features for gender and age classification. Algorithm evaluations on three publicly available databases (the FERET database, the LFW database and the FRCVv2 database) demonstrate the superior performance of the proposed method in both laboratory and unconstrained environments. In order to achieve enhanced robustness, a two-source photometric stereo method has been introduced to recover surface normals such that more invariant 3D facia features become available that can further boost classification accuracy and robustness. A 2D+3D imaging system has been designed for construction of a self-collected dataset including 2D and 3D facial data. Experiments show that utilisation of 3D facial features can increase gender classification rate by up to 6% (based on the self-collected dataset), and can increase age classification rate by up to 12% (based on the Photoface database). Finally, two case study HCI systems, a gaze gesture based map browser and a directed advertising billboard, have been designed by adopting all the proposed algorithms as well as the fully compatible imaging system. Benefits from the proposed algorithms naturally ensure that the case study systems can possess high robustness to head pose variation and illumination variation; and can achieve excellent real-time performance. Overall, the proposed HCI strategy enabled by reliably recognised facial cues can serve to spawn a wide array of innovative systems and to bring HCI to a more natural and intelligent state

Human-Machine Interfaces for Service Robotics

L'abstract è presente nell'allegato / the abstract is in the attachmen

Toward multimodality: gesture and vibrotactile feedback in natural human computer interaction

In the present work, users’ interaction with advanced systems has been investigated in different application domains and with respect to different interfaces. The methods employed were carefully devised to respond to the peculiarities of the interfaces under examination. We could extract a set of recommendations for developers. The first application domain examined regards the home. In particular, we addressed the design of a gestural interface for controlling a lighting system embedded into a piece of furniture in the kitchen. A sample of end users was observed while interacting with the virtual simulation of the interface. Based on the videoanalysis of users’ spontaneous behaviors, we could derive a set of significant interaction trends The second application domain involved the exploration of an urban environment in mobility. In a comparative study, a haptic-audio interface and an audio-visual interface were employed for guiding users towards landmarks and for providing them with information. We showed that the two systems were equally efficient in supporting the users and they were both well- received by them. In a navigational task we compared two tactile displays each embedded in a different wearable device, i.e., a glove and a vest. Despite the differences in the shape and size, both systems successfully directed users to the target. The strengths and the flaws of the two devices were pointed out and commented by users. In a similar context, two devices supported Augmented Reality technology, i.e., a pair of smartglasses and a smartphone, were compared. The experiment allowed us to identify the circumstances favoring the use of smartglasses or the smartphone. Considered altogether, our findings suggest a set of recommendations for developers of advanced systems. First, we outline the importance of properly involving end users for unveiling intuitive interaction modalities with gestural interfaces. We also highlight the importance of providing the user the chance to choose the interaction mode better fitting the contextual characteristics and to adjust the features of every interaction mode. Finally, we outline the potential of wearable devices to support interactions on the move and the importance of finding a proper balance between the amount of information conveyed to the user and the size of the device

Teaching Unknown Objects by Leveraging Human Gaze and Augmented Reality in Human-Robot Interaction

Roboter finden aufgrund ihrer außergewöhnlichen Arbeitsleistung, Präzision, Effizienz und Skalierbarkeit immer mehr Verwendung in den verschiedensten Anwendungsbereichen. Diese Entwicklung wurde zusätzlich begünstigt durch Fortschritte in der Künstlichen Intelligenz (KI), insbesondere im Maschinellem Lernen (ML). Mit Hilfe moderner neuronaler Netze sind Roboter in der Lage, Objekte in ihrer Umgebung zu erkennen und mit ihnen zu interagieren. Ein erhebliches Manko besteht jedoch darin, dass das Training dieser Objekterkennungsmodelle, in aller Regel mit einer zugrundeliegenden Abhängig von umfangreichen Datensätzen und der Verfügbarkeit großer Datenmengen einhergeht. Dies ist insbesondere dann problematisch, wenn der konkrete Einsatzort des Roboters und die Umgebung, einschließlich der darin befindlichen Objekte, nicht im Voraus bekannt sind. Die breite und ständig wachsende Palette von Objekten macht es dabei praktisch unmöglich, das gesamte Spektrum an existierenden Objekten allein mit bereits zuvor erstellten Datensätzen vollständig abzudecken. Das Ziel dieser Dissertation war es, einem Roboter unbekannte Objekte mit Hilfe von Human-Robot Interaction (HRI) beizubringen, um ihn von seiner Abhängigkeit von Daten sowie den Einschränkungen durch vordefinierte Szenarien zu befreien. Die Synergie von Eye Tracking und Augmented Reality (AR) ermöglichte es dem als Lehrer fungierenden Menschen, mit dem Roboter zu kommunizieren und ihn mittels des menschlichen Blickes auf Objekte hinzuweisen. Dieser holistische Ansatz ermöglichte die Konzeption eines multimodalen HRI-Systems, durch das der Roboter Objekte identifizieren und dreidimensional segmentieren konnte, obwohl sie ihm zu diesem Zeitpunkt noch unbekannt waren, um sie anschließend aus unterschiedlichen Blickwinkeln eigenständig zu inspizieren. Anhand der Klasseninformationen, die ihm der Mensch mitteilte, war der Roboter daraufhin in der Lage, die entsprechenden Objekte zu erlernen und später wiederzuerkennen. Mit dem Wissen, das dem Roboter durch diesen auf HRI basierenden Lehrvorgang beigebracht worden war, war dessen Fähigkeit Objekte zu erkennen vergleichbar mit den Fähigkeiten modernster Objektdetektoren, die auf umfangreichen Datensätzen trainiert worden waren. Dabei war der Roboter jedoch nicht auf vordefinierte Klassen beschränkt, was seine Vielseitigkeit und Anpassungsfähigkeit unter Beweis stellte. Die im Rahmen dieser Dissertation durchgeführte Forschung leistete bedeutende Beiträge an der Schnittstelle von Machine Learning (ML), AR, Eye Tracking und Robotik. Diese Erkenntnisse tragen nicht nur zum besseren Verständnis der genannten Felder bei, sondern ebnen auch den Weg für weitere interdisziplinäre Forschung. Die in dieser Dissertation enthalten wissenschaftlichen Artikel wurden auf hochrangigen Konferenzen in den Bereichen Robotik, Eye Tracking und HRI veröffentlicht.Robots are becoming increasingly popular in a wide range of environments due to their exceptional work capacity, precision, efficiency, and scalability. This development has been further encouraged by advances in Artificial Intelligence (AI), particularly Machine Learning (ML). By employing sophisticated neural networks, robots are given the ability to detect and interact with objects in their vicinity. However, a significant drawback arises from the underlying dependency on extensive datasets and the availability of substantial amounts of training data for these object detection models. This issue becomes particularly problematic when the specific deployment location of the robot and the surroundings, including the objects within it, are not known in advance. The vast and ever-expanding array of objects makes it virtually impossible to comprehensively cover the entire spectrum of existing objects using preexisting datasets alone. The goal of this dissertation was to teach a robot unknown objects in the context of Human-Robot Interaction (HRI) in order to liberate it from its data dependency, unleashing it from predefined scenarios. In this context, the combination of eye tracking and Augmented Reality (AR) created a powerful synergy that empowered the human teacher to seamlessly communicate with the robot and effortlessly point out objects by means of human gaze. This holistic approach led to the development of a multimodal HRI system that enabled the robot to identify and visually segment the Objects of Interest (OOIs) in three-dimensional space, even though they were initially unknown to it, and then examine them autonomously from different angles. Through the class information provided by the human, the robot was able to learn the objects and redetect them at a later stage. Due to the knowledge gained from this HRI based teaching process, the robot’s object detection capabilities exhibited comparable performance to state-of-the-art object detectors trained on extensive datasets, without being restricted to predefined classes, showcasing its versatility and adaptability. The research conducted within the scope of this dissertation made significant contributions at the intersection of ML, AR, eye tracking, and robotics. These findings not only enhance the understanding of these fields, but also pave the way for further interdisciplinary research. The scientific articles included in this dissertation have been published at high-impact conferences in the fields of robotics, eye tracking, and HRI

Robotic surgery, human fallibility, and the politics of care

Robotic Surgery, Human Fallibility, and the Politics of Care leverages the methods and theoretical paradigms of performance, visual, and new media studies to explore the contradictions, aspirations, and failures of modern technologized medicine. In particular, I consider the use of robots in the operating rooms of a large research hospital. University Hospital illuminates a contemporary articulation of human bodies and robotic technology that focuses and amplifies existing and emergent tensions and contradictions in modern medicine's investment in providing both care and cure. Intuitive Surgical, Inc.'s da Vinci Surgical System provides a platform for this exploration, both as a concrete, material, and particular assemblage of hardware, software and human wetware, and as a technology that offers a specific and perhaps more productive vantage point--a modest step stool--for understanding the contemporary politics of surgical pedagogy and practice. I locate the dVSS in a broader context of ambivalence that surgeons experience with regard to the manual practices of their craft, an ambivalence amplified by the increasing sophistication and automation of surgical tools and the changing ontologies of surgical practice. The surgical interface of the dVSS prosthetically enhances--as well as displaces and replaces--embodied surgical skill. At a time when all facets of medical care grapple with the problem of medical error, I outline an emergent sensibility of machinic virtuosity, articulated to both human and robotic surgical practice alike, geared toward addressing and overcoming the perceived pitfalls of human fallibility. Rather than simply enacting a technological dehumanization of medicine, robotic surgery suggests a more complicated terrain where the nature of the human and the machine bleed into each other. What I term the becoming machine of the surgeon and the becoming surgeon of the medical device occurs on the cutting edge of the robot-surgeon interface. The implications of this emergent medical sensibility are far from clear or unilateral. In closing, I reflect on the uncertain impact of the ideal of machinic virtuosity on the politics of care. This reflection considers software and machine ethics alongside medicine's aspiration to manage contingency according to the procedurality of medical and surgical protocols

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

Safe and Sound: Proceedings of the 27th Annual International Conference on Auditory Display

Complete proceedings of the 27th International Conference on Auditory Display (ICAD2022), June 24-27. Online virtual conference

Haptics: Science, Technology, Applications

This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility