Feature selection of facial displays for detection of non verbal communication in natural conversation

Recognition of human communication has previously focused on deliberately acted emotions or in structured or artificial social contexts. This makes the result hard to apply to realistic social situations. This paper describes the recording of spontaneous human communication in a specific and common social situation: conversation between two people. The clips are then annotated by multiple observers to reduce individual variations in interpretation of social signals. Temporal and static features are generated from tracking using heuristic and algorithmic methods. Optimal features for classifying examples of spontaneous communication signals are then extracted by AdaBoost. The performance of the boosted classifier is comparable to human performance for some communication signals, even on this challenging and realistic data set

Image features and learning algorithms for biological, generic and social object recognition

Automated recognition of object categories in images is a critical step for many real-world computer vision applications. Interest region detectors and region descriptors have been widely employed to tackle the variability of objects in pose, scale, lighting, texture, color, and so on. Different types of object recognition problems usually require different image features and corresponding learning algorithms. This dissertation focuses on the design, evaluation and application of new image features and learning algorithms for the recognition of biological, generic and social objects. The first part of the dissertation introduces a new structure-based interest region detector called the principal curvature-based region detector (PCBR) which detects stable watershed regions that are robust to local intensity perturbations. This detector is specifically designed for region detection for biological objects. Several recognition architectures are then developed that fuse visual information from disparate types of image features for the categorization of complex objects. The described image features and learning algorithms achieve excellent performance on the difficult stonefly larvae dataset. The second part of the dissertation presents studies of methods for visual codebook learning and their application to object recognition. The dissertation first introduces the methodology and application of generative visual codebooks for stonefly recognition and introduces a discriminative evaluation methodology based on a maximum mutual information criterion. Then a new generative/discriminative visual codebook learning algorithm, called iterative discriminative clustering (IDC), is presented that refines the centers and the shapes of the generative codewords for improved discriminative power. It is followed by a novel codebook learning algorithm that builds multiple codebooks that are non-redundant in discriminative power. All these visual codebook learning algorithms achieve high performance on both biological and generic object recognition tasks. The final part of the dissertation describes a socially-driven clothes recognition system for an intelligent fitting-room system. The dissertation presents the results of a user study to identify the key factors for clothes recognition. It then describes learning algorithms for recognizing these key factors from clothes images using various image features. The clothes recognition system successfully enables automated social fashion information retrieval for an enhanced clothes shopping experience

Hand gesture recognition using deep learning neural networks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonHuman Computer Interaction (HCI) is a broad field involving different types of interactions including gestures. Gesture recognition concerns non-verbal motions used as a means of communication in HCI. A system may be utilised to identify human gestures to convey information for device control. This represents a significant field within HCI involving device interfaces and users. The aim of gesture recognition is to record gestures that are formed in a certain way and then detected by a device such as a camera. Hand gestures can be used as a form of communication for many different applications. It may be used by people who possess different disabilities, including those with hearing-impairments, speech impairments and stroke patients, to communicate and fulfil their basic needs. Various studies have previously been conducted relating to hand gestures. Some studies proposed different techniques to implement the hand gesture experiments. For image processing there are multiple tools to extract features of images, as well as Artificial Intelligence which has varied classifiers to classify different types of data. 2D and 3D hand gestures request an effective algorithm to extract images and classify various mini gestures and movements. This research discusses this issue using different algorithms. To detect 2D or 3D hand gestures, this research proposed image processing tools such as Wavelet Transforms and Empirical Mode Decomposition to extract image features. The Artificial Neural Network (ANN) classifier which used to train and classify data besides Convolutional Neural Networks (CNN). These methods were examined in terms of multiple parameters such as execution time, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood, negative likelihood, receiver operating characteristic, area under ROC curve and root mean square. This research discusses four original contributions in the field of hand gestures. The first contribution is an implementation of two experiments using 2D hand gesture video where ten different gestures are detected in short and long distances using an iPhone 6 Plus with 4K resolution. The experiments are performed using WT and EMD for feature extraction while ANN and CNN for classification. The second contribution comprises 3D hand gesture video experiments where twelve gestures are recorded using holoscopic imaging system camera. The third contribution pertains experimental work carried out to detect seven common hand gestures. Finally, disparity experiments were performed using the left and the right 3D hand gesture videos to discover disparities. The results of comparison show the accuracy results of CNN being 100% compared to other techniques. CNN is clearly the most appropriate method to be used in a hand gesture system.Imam Abdulrahman bin Faisal Universit

Everyday Automation

This Open Access book brings the experiences of automation as part of quotidian life into focus. It asks how, where and when automated technologies and systems are emerging in everyday life across different global regions? What are their likely impacts in the present and future? How do engineers, policy makers, industry stakeholders and designers envisage artificial intelligence (AI) and automated decision-making (ADM) as solutions to individual and societal problems? How do these future visions compare with the everyday realities, power relations and social inequalities in which AI and ADM are experienced? What do people know about automation and what are their experiences of engaging with ‘actually existing’ AI and ADM technologies? An international team of leading scholars bring together research developed across anthropology, sociology, media and communication studies and ethnology, which shows how by rehumanising automation, we can gain deeper understandings of its societal impacts

THE QUANTIFIED PANDEMIC: Digitised surveillance, containment and care in response to the COVID-19 crisis

In this chapter, I present a sociocultural analysis of how automated decision-making (ADM) tools and related software were deployed or anticipated in response to the COVID-19 crisis during the first year of the pandemic. These technologies included apps used to monitor people in quarantine and self-isolation, contact tracing apps, surveillance drones, digitised temperature checking devices, apps for delivering COVID test results, software for identifying ‘at risk’ patients and for selecting recipients of vaccines, and digital vaccine ‘passport’ apps, as well as automated symptom checker apps, platforms and chatbots designed to help people determine whether they were infected with the novel coronavirus or needed to seek medical attention. Building on scholarship in critical public health, technocultures and critical data studies, I identify and discuss the social and political contexts and effects of these technologies. I demonstrate that despite techno-utopian promissory narratives routinely promoting their advantages, while some of these technologies have assisted with COVID-19 surveillance, control and medical care, many have failed. Furthermore, the deployment of these technologies has in many cases exacerbated existing socioeconomic disadvantage and stigmatisation, excluded some social groups and populations from economic support or healthcare and flouted human rights relating to privacy and freedom of movement

Everyday Automation

This Open Access book brings the experiences of automation as part of quotidian life into focus. It asks how, where and when automated technologies and systems are emerging in everyday life across different global regions? What are their likely impacts in the present and future? How do engineers, policy makers, industry stakeholders and designers envisage artificial intelligence (AI) and automated decision-making (ADM) as solutions to individual and societal problems? How do these future visions compare with the everyday realities, power relations and social inequalities in which AI and ADM are experienced? What do people know about automation and what are their experiences of engaging with ‘actually existing’ AI and ADM technologies? An international team of leading scholars bring together research developed across anthropology, sociology, media and communication studies and ethnology, which shows how by rehumanising automation, we can gain deeper understandings of its societal impacts

Detection and the modern city

This dissertation examines detective fiction as a form which has evolved in close relation to the modern city from the nineteenth century to the present. The argument runs that the link between the urban setting and the detective story is an essential characteristic of the form which has been undervalued in the study of detective fiction. The importance of this relationship to the genre is delineated and emphasized through the use of representative examples, beginning with Edgar Allan Poe and then moving to Arthur Conan Doyle, Dashiell Hammett and finally a number of later writers in the field, all of whom use the city as setting for the narrative, as well as a problematizing element. The city can be a comfortably known environment wherein the detective operates, but it can also be a labyrinth of confusing forces and misleading clues. For the detective, whose goal is the solution of the puzzle, this environment causes by turn reassurance and distress. In a comparison between these authors, fundamental differences pertaining to the detective as individual and his interaction with the city are explored, and a development is described which sees the detective becoming increasingly unsure of the city and of his position within it. In terms of the genre, this relation shows how the detective becomes a figure who has to be dealt with in ever more complex terms, a shedding of the sureties of the past. On the personal level, the detective becomes a symbol of the modern individual in the city, who tries to make some sense of the living environment which the city offers, and the difficulties which the city creates for perception of the environment and the development of self-realization in terms of this environment. The study therefore operates on three levels: the formal, where the epistemology of the detective form is traced from early confidence to later manifestations of disruption of these confidences; the socio-urban, where the representation of the city is described as it changes; and the linked concern operating on the individualistic level, the development of the detective as unitary individual and "hero"

Remembering the City: An Augmented Reality Reconstruction of Memory, Power, and Identity in Ho Chi Minh City through Cartography & Architecture

Cartography and architecture are official channels that facilitate remembrance in Ho Chi Minh City. Maps and buildings serve as sites for actors of memory to manipulate the city\u27s narratives and shape its collective identity. Power enables the production of space and knowledge through sites of memory. The ruling regimes of Ho Chi Minh City have leveraged control over the natural environment and the local population to create new forms of materials that propagate their ideologies and ideals for the city. Alterations to the natural and built environments in the city legitimize the authorities\u27 official narratives for its history and future developments. This project explores the context and subtext of urban memory and its formation, using critical augmented reality to visualize the sites of memory. The design of the supplementary augmented reality application takes into consideration the computational theory behind the technology and the development tools for digital historical narratives. In addition, as this study investigates the complicity of science in promoting colonialism, imperialism, nationalism and uninformed nostalgia within the urban setting, it also critiques the use of a new form of technology, augmented reality, in memory formation and other historical processes. Augmented reality offers unprecedented potentials for history and other disciplines thanks to its accessibility and performance; however, the pitfalls of technology require developers and users to remain aware of the implications and assumptions behind each design