84 research outputs found

    Handbook of Digital Face Manipulation and Detection

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    This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

    Handbook of Digital Face Manipulation and Detection

    Get PDF
    This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

    Sensory History Matters for Visual Representation: Implications for Autism

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    How does the brain represent the enormous variety of the visual world? An approach to this question recognizes the types of information that visual representations maintain. The work in this thesis begins by investigating the neural correlates of perceptual similarity & distinctiveness, using EEG measurements of the evoked response to faces. In considering our results, we recognized that the effects being measured shared intrinsic relationships, both in measurement and in their theoretic basis. Using carry-over fMRI designs, we explored this relationship, ultimately demonstrating a new perspective on stimulus relationships based around sensory history that best explains the modulation of brain responses being measured. The result of this collection of experiments is a unified model of neural response modulation based around the integration of recent sensory history into a continually-updated reference; a drifting-norm. With this novel framework for understanding neural dynamics, we tested whether cognitive theories of autism spectrum disorder (ASD) might have a foundation in altered neural coding for perceptual information. Our results suggest ASD brain responses depend on a more moment-to-moment understanding of the visual world relative to neurotypical controls. This application both provides an exciting foothold in the brain for future investigations into the etiology of ASD, and validates the importance of sensory history as a dimension of visual representation

    Improving Access and Mental Health for Youth Through Virtual Models of Care

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    The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

    The Impact of Digital Technologies on Public Health in Developed and Developing Countries

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    This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

    An OpenISS Framework Specialization for Deep Learning-based Person Re-identification

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    Person detection and person re-identi�cation are rapidly increasing research areas in computer vision. They are independent but related. In fact, the output of person detection is the input of person re-identi�cation. There are a certain number of solutions for each of these two individual tasks. But currently, there is no existing solution that can combine them to form an integrated working pipeline. To �ll the gap, we propose a highly modular and structural framework solution that provides the functionalities including not only cross-language invocation and pipeline execution mechanism but also viewer, device, tracker, detector, and recognizer abstraction. We instantiate the proposed framework to achieve our goal of tracking the same person across multiple cameras, which essentially is the combination of person detection and person re-identi�cation. Besides the main task of person re-identi�cation, we also support skeleton tracking, as well as camera calibration, image alignment and green screen image which commonly comes with a computer vision framework. We evaluate our proposed solution according to the requirements and usage scenarios and report the major metrics used by the research community for person detection and person re-identi�cation tasks, respectively

    The Impact of Digital Technologies on Public Health in Developed and Developing Countries

    Get PDF
    This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

    Dance performance in cyberspace - transfer and transformation

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    The aim of this research undertaking is to understand the potential development of dance performance in the context of cyberculture, by examining the way practitioners use new media to create artworks that include audience participation, and by endeavouring in their theorization. With specific reference to cyberspace as a concept of electronic, networked and navigable space, the enquiry traces the connections such practices have with conventions of the medium of dance, which operate in its widely known condition as a live performing art. But acknowledgement that new media and new contexts of production and reception inform the characteristics of these artworks and their discursive articulation, in terms of the way people and digital technologies interact in contemporary culture, is a major principle to their analysis and evaluation. This qualitative research is based on case-study design as a means of finding pragmatic evidence in particulars, to illustrate abstract concepts, technological processes and aesthetic values that are underway in a new area of knowledge. The field where this research operates within is located by a mapping of published literature that informs a theoretical interdisciplinary framework, which contextualizes the interpretation of artworks. The selected case studies have been subject to a process of systematic and detailed analysis, entailed with a model devised for the purpose of this enquiry. From this undertaking it can be claimed that while an extensive array of technologies, media and interactive models is available in this field, the artists pursue a commitment to demonstrate their worth for specifically developing (new media) dance performance, and for dance performance to articulate technological and critical issues for cyberculture studies. The results of this enquiry also contribute to conceptual understanding of what dance can be, today, in the light of technological changes

    Biometric face recognition using multilinear projection and artificial intelligence

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    PhD ThesisNumerous problems of automatic facial recognition in the linear and multilinear subspace learning have been addressed; nevertheless, many difficulties remain. This work focuses on two key problems for automatic facial recognition and feature extraction: object representation and high dimensionality. To address these problems, a bidirectional two-dimensional neighborhood preserving projection (B2DNPP) approach for human facial recognition has been developed. Compared with 2DNPP, the proposed method operates on 2-D facial images and performs reductions on the directions of both rows and columns of images. Furthermore, it has the ability to reveal variations between these directions. To further improve the performance of the B2DNPP method, a new B2DNPP based on the curvelet decomposition of human facial images is introduced. The curvelet multi- resolution tool enhances the edges representation and other singularities along curves, and thus improves directional features. In this method, an extreme learning machine (ELM) classifier is used which significantly improves classification rate. The proposed C-B2DNPP method decreases error rate from 5.9% to 3.5%, from 3.7% to 2.0% and from 19.7% to 14.2% using ORL, AR, and FERET databases compared with 2DNPP. Therefore, it achieves decreases in error rate more than 40%, 45%, and 27% respectively with the ORL, AR, and FERET databases. Facial images have particular natural structures in the form of two-, three-, or even higher-order tensors. Therefore, a novel method of supervised and unsupervised multilinear neighborhood preserving projection (MNPP) is proposed for face recognition. This allows the natural representation of multidimensional images 2-D, 3-D or higher-order tensors and extracts useful information directly from tensotial data rather than from matrices or vectors. As opposed to a B2DNPP which derives only two subspaces, in the MNPP method multiple interrelated subspaces are obtained over different tensor directions, so that the subspaces are learned iteratively by unfolding the tensor along the different directions. The performance of the MNPP has performed in terms of the two modes of facial recognition biometrics systems of identification and verification. The proposed supervised MNPP method achieved decrease over 50.8%, 75.6%, and 44.6% in error rate using ORL, AR, and FERET databases respectively, compared with 2DNPP. Therefore, the results demonstrate that the MNPP approach obtains the best overall performance in various learning scenarios
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