Ocular motion classification for mobile device presentation attack detection

Title from PDF of title page viewed February 25, 2021Dissertation advisor: Reza DerakhshanVitaIncludes bibliographical references (page 105-129)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2020As a practical pursuit of quantified uniqueness, biometrics explores the parameters that make us who we are and provides the tools we need to secure the integrity of that identity. In our culture of constant connectivity, an increasing reliance on biometrically secured mobile devices is transforming them into a target for bad actors. While no system will ever prevent all forms of intrusion, even state of the art biometric methods remain vulnerable to spoof attacks. As these attacks become more sophisticated, ocular motion based presentation attack detection (PAD) methods provide a potential deterrent. This dissertation presents the methods and evaluation of a novel optokinetic nystagmus (OKN) based PAD system for mobile device applications which leverages phase-locked temporal features of a unique reflexive behavioral response. Background is provided for historical and literary context of eye motion and ocular tracking to provide context to the objectives and accomplishments of this work. An evaluation of the improved methods for sample processing and sequential stability is provided with highlights for the presented improvements to the stability of convolutional facial landmark localization, and automated spatiotemporal feature extraction and classification models. Insights gleaned from this work are provided to elucidate some of the major challenges of mobile ocular motion feature extraction, as well as additional future considerations for the refinement and application of OKN motion signatures as a novel mobile device based PAD method.Introduction -- Retrospective, Contextual and Contemporary analysis -- Experimental Design -- Methods and Results -- Discussion -- Conclusion

Drawing, Handwriting Processing Analysis: New Advances and Challenges

International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline

Moving usable security research out of the lab: evaluating the use of VR studies for real-world authentication research

Empirical evaluations of real-world research artefacts that derive results from observations and experiments are a core aspect of usable security research. Expert interviews as part of this thesis revealed that the costs associated with developing and maintaining physical research artefacts often amplify human-centred usability and security research challenges. On top of that, ethical and legal barriers often make usability and security research in the field infeasible. Researchers have begun simulating real-life conditions in the lab to contribute to ecological validity. However, studies of this type are still restricted to what can be replicated in physical laboratory settings. Furthermore, historically, user study subjects were mainly recruited from local areas only when evaluating hardware prototypes. The human-centred research communities have recognised and partially addressed these challenges using online studies such as surveys that allow for the recruitment of large and diverse samples as well as learning about user behaviour. However, human-centred security research involving hardware prototypes is often concerned with human factors and their impact on the prototypes’ usability and security, which cannot be studied using traditional online surveys. To work towards addressing the current challenges and facilitating research in this space, this thesis explores if – and how – virtual reality (VR) studies can be used for real-world usability and security research. It first validates the feasibility and then demonstrates the use of VR studies for human-centred usability and security research through six empirical studies, including remote and lab VR studies as well as video prototypes as part of online surveys. It was found that VR-based usability and security evaluations of authentication prototypes, where users provide touch, mid-air, and eye-gaze input, greatly match the findings from the original real-world evaluations. This thesis further investigated the effectiveness of VR studies by exploring three core topics in the authentication domain: First, the challenges around in-the-wild shoulder surfing studies were addressed. Two novel VR shoulder surfing methods were implemented to contribute towards realistic shoulder surfing research and explore the use of VR studies for security evaluations. This was found to allow researchers to provide a bridge over the methodological gap between lab and field studies. Second, the ethical and legal barriers when conducting in situ usability research on authentication systems were addressed. It was found that VR studies can represent plausible authentication environments and that a prototype’s in situ usability evaluation results deviate from traditional lab evaluations. Finally, this thesis contributes a novel evaluation method to remotely study interactive VR replicas of real-world prototypes, allowing researchers to move experiments that involve hardware prototypes out of physical laboratories and potentially increase a sample’s diversity and size. The thesis concludes by discussing the implications of using VR studies for prototype usability and security evaluations. It lays the foundation for establishing VR studies as a powerful, well-evaluated research method and unfolds its methodological advantages and disadvantages

Eye Tracking Methods for Analysis of Visuo-Cognitive Behavior in Medical Imaging

Predictive modeling of human visual search behavior and the underlying metacognitive processes is now possible thanks to significant advances in bio-sensing device technology and machine intelligence. Eye tracking bio-sensors, for example, can measure psycho-physiological response through change events in configuration of the human eye. These events include positional changes such as visual fixation, saccadic movements, and scanpath, and non-positional changes such as blinks and pupil dilation and constriction. Using data from eye-tracking sensors, we can model human perception, cognitive processes, and responses to external stimuli. In this study, we investigated the visuo-cognitive behavior of clinicians during the diagnostic decision process for breast cancer screening under clinically equivalent experimental conditions involving multiple monitors and breast projection views. Using a head-mounted eye tracking device and a customized user interface, we recorded eye change events and diagnostic decisions from 10 clinicians (three breast-imaging radiologists and seven Radiology residents) for a corpus of 100 screening mammograms (comprising cases of varied pathology and breast parenchyma density). We proposed novel features and gaze analysis techniques, which help to encode discriminative pattern changes in positional and non-positional measures of eye events. These changes were shown to correlate with individual image readers' identity and experience level, mammographic case pathology and breast parenchyma density, and diagnostic decision. Furthermore, our results suggest that a combination of machine intelligence and bio-sensing modalities can provide adequate predictive capability for the characterization of a mammographic case and image readers diagnostic performance. Lastly, features characterizing eye movements can be utilized for biometric identification purposes. These findings are impactful in real-time performance monitoring and personalized intelligent training and evaluation systems in screening mammography. Further, the developed algorithms are applicable in other application domains involving high-risk visual tasks

Perceptual Manipulations for Hiding Image Transformations in Virtual Reality

Users of a virtual reality make frequent gaze shifts and head movements to explore their surrounding environment. Saccades are rapid, ballistic, conjugate eye movements that reposition our gaze, and in doing so create large-field motion on our retina. Due to the high speed motion on the retina during saccades, the brain suppresses the visual signals from the eye, a perceptual phenomenon known as the saccadic suppression. These moments of visual blindness can help hide the display graphical updates in a virtual reality. In this dissertation, I investigated how the visibility of various image transformations differed, during combinations of saccade and head rotation conditions. Additionally, I studied how hand and gaze interaction, affected image change discrimination in an inattentional blindness task. I conducted four psychophysical experiments in desktop or head-mounted VR. In the eye tracking studies, users viewed 3D scenes, and were triggered to make a vertical or horizontal saccade. During the saccade an instantaneous translation or rotation was applied to the virtual camera used to render the scene. Participants were required to indicate the direction of these transitions after each trial. The results showed that type and size of the image transformation affected change detectability. During horizontal or vertical saccades, rotations along the roll axis were the most detectable, while horizontal and vertical translations were least noticed. In a second similar study, I added a constant camera motion to simulate a head rotation, and in a third study, I compared active head rotation with a simulated rotation or a static head. I found less sensitivity to transsaccadic horizontal compared to vertical camera shifts during simulated or real head pan. Conversely, during simulated or real head tilt observers were less sensitive to transsaccadic vertical than horizontal camera shifts. In addition, in my multi-interactive inattentional blindness experiment, I compared sensitivity to sudden image transformations when a participant used their hand and gaze to move and watch an object, to when they only watched it move. The results confirmed that when involved in a primary task that requires focus and attention with two interaction modalities (gaze and hand), a visual stimuli can better be hidden than when only one sense (vision) is involved. Understanding the effect of continuous head movement and attention on the visibility of a sudden transsaccadic change can help optimize the visual performance of gaze-contingent displays and improve user experience. Perceptually suppressed rotations or translations can be used to introduce imperceptible changes in virtual camera pose in applications such as networked gaming, collaborative virtual reality and redirected walking. This dissertation suggests that such transformations can be more effective and more substantial during active or passive head motion. Moreover, inattentional blindness during an attention-demanding task provides additional opportunities for imperceptible updates to a visual display

Perceptual Manipulations for Hiding Image Transformations in Virtual Reality

Users of a virtual reality make frequent gaze shifts and head movements to explore their surrounding environment. Saccades are rapid, ballistic, conjugate eye movements that reposition our gaze, and in doing so create large-field motion on our retina. Due to the high speed motion on the retina during saccades, the brain suppresses the visual signals from the eye, a perceptual phenomenon known as the saccadic suppression. These moments of visual blindness can help hide the display graphical updates in a virtual reality. In this dissertation, I investigated how the visibility of various image transformations differed, during combinations of saccade and head rotation conditions. Additionally, I studied how hand and gaze interaction, affected image change discrimination in an inattentional blindness task. I conducted four psychophysical experiments in desktop or head-mounted VR. In the eye tracking studies, users viewed 3D scenes, and were triggered to make a vertical or horizontal saccade. During the saccade an instantaneous translation or rotation was applied to the virtual camera used to render the scene. Participants were required to indicate the direction of these transitions after each trial. The results showed that type and size of the image transformation affected change detectability. During horizontal or vertical saccades, rotations along the roll axis were the most detectable, while horizontal and vertical translations were least noticed. In a second similar study, I added a constant camera motion to simulate a head rotation, and in a third study, I compared active head rotation with a simulated rotation or a static head. I found less sensitivity to transsaccadic horizontal compared to vertical camera shifts during simulated or real head pan. Conversely, during simulated or real head tilt observers were less sensitive to transsaccadic vertical than horizontal camera shifts. In addition, in my multi-interactive inattentional blindness experiment, I compared sensitivity to sudden image transformations when a participant used their hand and gaze to move and watch an object, to when they only watched it move. The results confirmed that when involved in a primary task that requires focus and attention with two interaction modalities (gaze and hand), a visual stimuli can better be hidden than when only one sense (vision) is involved. Understanding the effect of continuous head movement and attention on the visibility of a sudden transsaccadic change can help optimize the visual performance of gaze-contingent displays and improve user experience. Perceptually suppressed rotations or translations can be used to introduce imperceptible changes in virtual camera pose in applications such as networked gaming, collaborative virtual reality and redirected walking. This dissertation suggests that such transformations can be more effective and more substantial during active or passive head motion. Moreover, inattentional blindness during an attention-demanding task provides additional opportunities for imperceptible updates to a visual display

Investigating Real-time Touchless Hand Interaction and Machine Learning Agents in Immersive Learning Environments

The recent surge in the adoption of new technologies and innovations in connectivity, interaction technology, and artificial realities can fundamentally change the digital world. eXtended Reality (XR), with its potential to bridge the virtual and real environments, creates new possibilities to develop more engaging and productive learning experiences. Evidence is emerging that thissophisticated technology offers new ways to improve the learning process for better student interaction and engagement. Recently, immersive technology has garnered much attention as an interactive technology that facilitates direct interaction with virtual objects in the real world. Furthermore, these virtual objects can be surrogates for real-world teaching resources, allowing for virtual labs. Thus XR could enable learning experiences that would not bepossible in impoverished educational systems worldwide. Interestingly, concepts such as virtual hand interaction and techniques such as machine learning are still not widely investigated in immersive learning. Hand interaction technologies in virtual environments can support the kinesthetic learning pedagogical approach, and the need for its touchless interaction nature hasincreased exceptionally in the post-COVID world. By implementing and evaluating real-time hand interaction technology for kinesthetic learning and machine learning agents for self-guided learning, this research has addressed these underutilized technologies to demonstrate the efficiency of immersive learning. This thesis has explored different hand-tracking APIs and devices to integrate real-time hand interaction techniques. These hand interaction techniques and integrated machine learning agents using reinforcement learning are evaluated with different display devices to test compatibility. The proposed approach aims to provide self-guided, more productive, and interactive learning experiences. Further, this research has investigated ethics, privacy, and security issues in XR and covered the future of immersive learning in the Metaverse.<br/

Investigating Real-time Touchless Hand Interaction and Machine Learning Agents in Immersive Learning Environments

The recent surge in the adoption of new technologies and innovations in connectivity, interaction technology, and artificial realities can fundamentally change the digital world. eXtended Reality (XR), with its potential to bridge the virtual and real environments, creates new possibilities to develop more engaging and productive learning experiences. Evidence is emerging that thissophisticated technology offers new ways to improve the learning process for better student interaction and engagement. Recently, immersive technology has garnered much attention as an interactive technology that facilitates direct interaction with virtual objects in the real world. Furthermore, these virtual objects can be surrogates for real-world teaching resources, allowing for virtual labs. Thus XR could enable learning experiences that would not bepossible in impoverished educational systems worldwide. Interestingly, concepts such as virtual hand interaction and techniques such as machine learning are still not widely investigated in immersive learning. Hand interaction technologies in virtual environments can support the kinesthetic learning pedagogical approach, and the need for its touchless interaction nature hasincreased exceptionally in the post-COVID world. By implementing and evaluating real-time hand interaction technology for kinesthetic learning and machine learning agents for self-guided learning, this research has addressed these underutilized technologies to demonstrate the efficiency of immersive learning. This thesis has explored different hand-tracking APIs and devices to integrate real-time hand interaction techniques. These hand interaction techniques and integrated machine learning agents using reinforcement learning are evaluated with different display devices to test compatibility. The proposed approach aims to provide self-guided, more productive, and interactive learning experiences. Further, this research has investigated ethics, privacy, and security issues in XR and covered the future of immersive learning in the Metaverse.<br/