Cockpit Ocular Recording System (CORS)

The overall goal was the development of a Cockpit Ocular Recording System (CORS). Four tasks were used: (1) the development of the system; (2) the experimentation and improvement of the system; (3) demonstrations of the working system; and (4) system documentation. Overall, the prototype represents a workable and flexibly designed CORS system. For the most part, the hardware use for the prototype system is off-the-shelf. All of the following software was developed specifically: (1) setup software that the user specifies the cockpit configuration and identifies possible areas in which the pilot will look; (2) sensing software which integrates the 60 Hz data from the oculometer and heat orientation sensing unit; (3) processing software which applies a spatiotemporal filter to the lookpoint data to determine fixation/dwell positions; (4) data recording output routines; and (5) playback software which allows the user to retrieve and analyze the data. Several experiments were performed to verify the system accuracy and quantify system deficiencies. These tests resulted in recommendations for any future system that might be constructed

Flipping the world upside down: Using eye tracking in virtual reality to study visual search in inverted scenes

Image inversion is a powerful tool for investigating cognitive mechanisms of visual perception. However, studies have mainly used inversion in paradigms presented on two-dimensional computer screens. It remains open whether disruptive effects of inversion also hold true in more naturalistic scenarios. In our study, we used scene inversion in virtual reality in combination with eye tracking to investigate the mechanisms of repeated visual search through three-dimensional immersive indoor scenes. Scene inversion affected all gaze and head measures except fixation durations and saccade amplitudes. Our behavioral results, surprisingly, did not entirely follow as hypothesized: While search efficiency dropped significantly in inverted scenes, participants did not utilize more memory as measured by search time slopes. This indicates that despite the disruption, participants did not try to compensate the increased difficulty by using more memory. Our study highlights the importance of investigating classical experimental paradigms in more naturalistic scenarios to advance research on daily human behavior

A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms

In this paper a review is presented of the research on eye gaze estimation techniques and applications, that has progressed in diverse ways over the past two decades. Several generic eye gaze use-cases are identified: desktop, TV, head-mounted, automotive and handheld devices. Analysis of the literature leads to the identification of several platform specific factors that influence gaze tracking accuracy. A key outcome from this review is the realization of a need to develop standardized methodologies for performance evaluation of gaze tracking systems and achieve consistency in their specification and comparative evaluation. To address this need, the concept of a methodological framework for practical evaluation of different gaze tracking systems is proposed.Comment: 25 pages, 13 figures, Accepted for publication in IEEE Access in July 201

The Impact of Congruence between Brand and Celebrity Endorsers on Advertising Effectiveness: An Eye-tracking Study

Department of Human Factors EngineeringRecent studies on the role of celebrity endorsers in the advertising of attractiveness-related products (e.g. cosmetics, perfumes, etc.) have demonstrated that congruence between brand and celebrity affects advertising effectiveness. According to the previous research, less congruence causes the vampire effect defined as a decrease in the brand recall due to attraction towards a celebrity endorser in advertising. By far, studies on celebrity endorsement have only focused on congruence between brand and the physical appearance of a celebrity endorser (appearance congruence) based on the theoretical background of source attractiveness and match-up hypothesis. Today, however, meaning transfer model should be significantly considered because of the increase of a variety of communication windows between celebrity and consumers. In that sense, congruence between the brand value and celebrities??? personal image (value congruence) can influence advertising effectiveness, by making it fluent to retrieve the associative memory of advertising with celebrity based on an accessibility-diagnostic framework. Therefore, the present study investigates whether value congruence is as important as appearance congruence for improving advertising effectiveness and whether the congruences have an impact on the changed attention leading to advertising effectiveness. In this study, the main experiment was carried out with the newly recruited 24 female subjects. The stimuli for the main experiment was selected through the preliminary experiment. During the experiment, subjects were shown a series of 60 stimuli in the form of an advertisement poster, and their eye-gaze was recording. Next, subjects were requested to rank the level of appropriateness of each stimulus while their eye-gaze was tracked. After that, advertising recall and recognition tests were done, and they were also asked to evaluate value congruence as well as appearance congruence. Finally, subjects evaluated appropriateness, effectiveness, and liking of each stimulus. Results showed that both appearance congruence and value congruence were crucial for advertising effectiveness. Behavioral responses revealed that it was more appropriate for a celebrity who has both appearance congruence and value congruence to promote the brand, and especially, value congruence was more influential than appearance congruence. The eye-tracking data also implies that the memory regarding advertising can be encoded by associative celebrity endorser faster without the longer attention. In summary, the results indicated that value congruence could play an important role for more accurate recall. These findings suggest that value congruence is as important as value congruence for improvement of the advertising effectiveness. Therefore, to establish brand identity effectively, considering value congruence may enhance print advertising effectiveness beyond the level by simply focusing on appearance congruence.ope

Eye-Tracking in Interactive Virtual Environments: Implementation and Evaluation

Not all eye-tracking methodology and data processing are equal. While the use of eye-tracking is intricate because of its grounding in visual physiology, traditional 2D eye-tracking methods are supported by software, tools, and reference studies. This is not so true for eye-tracking methods applied in virtual reality (imaginary 3D environments). Previous research regarded the domain of eye-tracking in 3D virtual reality as an untamed realm with unaddressed issues. The present paper explores these issues, discusses possible solutions at a theoretical level, and offers example implementations. The paper also proposes a workflow and software architecture that encompasses an entire experimental scenario, including virtual scene preparation and operationalization of visual stimuli, experimental data collection and considerations for ambiguous visual stimuli, post-hoc data correction, data aggregation, and visualization. The paper is accompanied by examples of eye-tracking data collection and evaluation based on ongoing research of indoor evacuation behavior

Biomove: Biometric user identification from human kinesiological movements for virtual reality systems

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Virtual reality (VR) has advanced rapidly and is used for many entertainment and business purposes. The need for secure, transparent and non-intrusive identification mechanisms is important to facilitate users’ safe participation and secure experience. People are kinesiologically unique, having individual behavioral and movement characteristics, which can be leveraged and used in security sensitive VR applications to compensate for users’ inability to detect potential observational attackers in the physical world. Additionally, such method of identification using a user’s kinesiological data is valuable in common scenarios where multiple users simultaneously participate in a VR environment. In this paper, we present a user study (n = 15) where our participants performed a series of controlled tasks that require physical movements (such as grabbing, rotating and dropping) that could be decomposed into unique kinesiological patterns while we monitored and captured their hand, head and eye gaze data within the VR environment. We present an analysis of the data and show that these data can be used as a biometric discriminant of high confidence using machine learning classification methods such as kNN or SVM, thereby adding a layer of security in terms of identification or dynamically adapting the VR environment to the users’ preferences. We also performed a whitebox penetration testing with 12 attackers, some of whom were physically similar to the participants. We could obtain an average identification confidence value of 0.98 from the actual participants’ test data after the initial study and also a trained model classification accuracy of 98.6%. Penetration testing indicated all attackers resulted in confidence values of less than 50% (\u3c50%), although physically similar attackers had higher confidence values. These findings can help the design and development of secure VR systems

A Real-Time Video-based Eye Tracking Approach for Driver Attention Study

nowing the driver's point of gaze has significant potential to enhance driving safety, eye movements can be used as an indicator of the attention state of a driver; but the primary obstacle of integrating eye gaze into today's large scale real world driving attention study is the availability of a reliable, low-cost eye-tracking system. In this paper, we make an attempt to investigate such a real-time system to collect driver's eye gaze in real world driving environment. A novel eye-tracking approach is proposed based on low cost head mounted eye tracker. Our approach detects corneal reflection and pupil edge points firstly, and then fits the points with ellipse. The proposed approach is available in different illumination and driving environment from simple inexpensive head mounted eye tracker, which can be widely used in large scale experiments. The experimental results illustrate our approach can reliably estimate eye position with an accuracy of average 0.34 degree of visual angle in door experiment and 2--5 degrees in real driving environments

Deep face tracking and parsing in the wild

Face analysis has been a long-standing research direction in the field of computer vision and pattern recognition. A complete face analysis system involves solving several tasks including face detection, face tracking, face parsing, and face recognition. Recently, the performance of methods in all tasks has significantly improved thanks to the employment of Deep Convolutional Neural Networks (DCNNs). However, existing face analysis algorithms mainly focus on solving facial images captured in the constrained laboratory environment, and their performance on real-world images has remained less explored. Compared with the lab environment, the in-the-wild settings involve greater diversity in face sizes, poses, facial expressions, background clutters, lighting conditions and imaging quality. This thesis investigates two fundamental tasks in face analysis under in-the-wild settings: face tracking and face parsing. Both tasks serve as important prerequisites for downstream face analysis applications. However, in-the-wild datasets remain scarce in both fields and models have not been rigorously evaluated in such settings. In this thesis, we aim to bridge that gap of lacking in-the-wild data, evaluate existing methods in these settings, and develop accurate, robust and efficient deep learning-based methods for the two tasks. For face tracking in the wild, we introduce the first in-the-wild face tracking dataset, MobiFace, that consists of 80 videos captured by mobile phones during mobile live-streaming. The environment of the live-streaming performance is fully unconstrained and the interactions between users and mobile phones are natural and spontaneous. Next, we evaluate existing tracking methods, including generic object trackers and dedicated face trackers. The results show that MobiFace represent unique challenges in face tracking in the wild and cannot be readily solved by existing methods. Finally, we present a DCNN-based framework, FT-RCNN, that significantly outperforms other methods in face tracking in the wild. For face parsing in the wild, we introduce the first large-scale in-the-wild face dataset, iBugMask, that contains 21, 866 training images and 1, 000 testing images. Unlike existing datasets, the images in iBugMask are captured in the fully unconstrained environment and are not cropped or preprocessed of any kind. Manually annotated per-pixel labels for eleven facial regions are provided for each target face. Next, we benchmark existing parsing methods and the results show that iBugMask is extremely challenging for all methods. By rigorous benchmarking, we observe that the pre-processing of facial images with bounding boxes in face parsing in the wild introduces bias. When cropping the face with a bounding box, a cropping margin has to be hand-picked. If face alignment is used, fiducial landmarks are required and a predefined alignment template has to be selected. These additional hyper-parameters have to be carefully considered and can have a significant impact on the face parsing performance. To solve this, we propose Region-of-Interest (RoI) Tanh-polar transform that warps the whole image to a fixed-sized representation. Moreover, the RoI Tanh-polar transform is differentiable and allows for rotation equivariance in 1 DCNNs. We show that when coupled with a simple Fully Convolutional Network, our RoI Tanh-polar transformer Network has achieved state-of-the-art results on face parsing in the wild. This thesis contributes towards in-the-wild face tracking and face parsing by providing novel datasets and proposing effective frameworks. Both tasks can benefit real-world downstream applications such as facial age estimation, facial expression recognition and lip-reading. The proposed RoI Tanh-polar transform also provides a new perspective in how to preprocess the face images and make the DCNNs truly end-to-end for real-world face analysis applications.Open Acces