Eye tracking based on ultrasonic position measurement in head free video-based eye-gaze detection

2004 ◽  
Author(s):  
D. Ishima ◽  
Y. Ebisawa
Keyword(s):  
Eye Tracking ◽  
Eye Gaze ◽  
Position Measurement ◽  
Gaze Detection

Integrated Approach for Sclera Recognition and Eye Gaze Detection

2018 ◽  
Vol 6 (7) ◽  
pp. 77
Author(s):  
Snehal S. Rajole ◽  
J. V. Shinde
Keyword(s):  
Near Infrared ◽  
Eye Gaze ◽  
Integrated Approach ◽  
Error Rates ◽  
Infrared Images ◽  
Scaling Method ◽  
Extensive Simulation ◽  
Sclera Recognition ◽  
Gaze Detection ◽  
Biometric Trait

In this paper we proposed unique technique which is adaptive to noisy images for eye gaze detection as processing noisy sclera images captured at-a-distance and on-the-move has not been extensively investigated. Sclera blood vessels have been investigated recently as an efficient biometric trait. Capturing part of the eye with a normal camera using visible-wavelength images rather than near infrared images has provoked research interest. This technique involves sclera template rotation alignment and a distance scaling method to minimize the error rates when noisy eye images are captured at-a-distance and on-the move. The proposed system is tested and results are generated by extensive simulation in java.

scholarly journals Techniques Used for Eye Gaze Interfaces and Survey

2015 ◽  
Vol 1 (6) ◽  
pp. 276
Author(s):  
Maria Rashid ◽  
Wardah Mehmood ◽  
Aliya Ashraf
Keyword(s):  
Eye Tracking ◽  
Eye Gaze ◽  
3D Models ◽  
Image Process ◽  
Usability Problems ◽  
Software Algorithms ◽  
Pupil Detection ◽  
Eye Movement Tracking ◽  
2D And 3D ◽  
Movement Tracking

Eye movement tracking is a method that is now-a-days used for checking the usability problems in the contexts of Human Computer Interaction (HCI). Firstly we present eye tracking technology and key elements.We tend to evaluate the behavior of the use when they are using the interace of eye gaze. Used different techniques i.e. electro-oculography, infrared oculography, video oculography, image process techniques, scrolling techniques, different models, probable approaches i.e. shape based approach, appearance based methods, 2D and 3D models based approach and different software algorithms for pupil detection etc. We have tried to compare the surveys based on their geometric properties and reportable accuracies and eventually we conclude this study by giving some prediction regarding future eye-gaze. We point out some techniques by using various eyes properties comprising nature, appearance and gesture or some combination for eye tracking and detection. Result displays eye-gaze technique is faster and better approach for selection than a mouse selection. Rate of error for all the matters determines that there have been no errors once choosing from main menus with eye mark and with mouse. But there have been a chance of errors when once choosing from sub menus in case of eye mark. So, maintain head constantly in front of eye gaze monitor.

scholarly journals Tracking eye-gaze in smart home systems (SHS): first insights from eye-tracking and self-report measures

2021 ◽  
Author(s):  
Federico Cassioli ◽  
Laura Angioletti ◽  
Michela Balconi
Keyword(s):  
Eye Tracking ◽  
Personality Traits ◽  
Smart Home ◽  
Near Infrared ◽  
Tracking System ◽  
Eye Gaze ◽  
Self Report ◽  
Visual Exploration ◽  
Living Room ◽  
Psychological Traits

AbstractHuman–computer interaction (HCI) is particularly interesting because full-immersive technology may be approached differently by users, depending on the complexity of the interaction, users’ personality traits, and their motivational systems inclination. Therefore, this study investigated the relationship between psychological factors and attention towards specific tech-interactions in a smart home system (SHS). The relation between personal psychological traits and eye-tracking metrics is investigated through self-report measures [locus of control (LoC), user experience (UX), behavioral inhibition system (BIS) and behavioral activation system (BAS)] and a wearable and wireless near-infrared illumination based eye-tracking system applied to an Italian sample (n = 19). Participants were asked to activate and interact with five different tech-interaction areas with different levels of complexity (entrance, kitchen, living room, bathroom, and bedroom) in a smart home system (SHS), while their eye-gaze behavior was recorded. Data showed significant differences between a simpler interaction (entrance) and a more complex one (living room), in terms of number of fixation. Moreover, slower time to first fixation in a multifaceted interaction (bathroom), compared to simpler ones (kitchen and living room) was found. Additionally, in two interaction conditions (living room and bathroom), negative correlations were found between external LoC and fixation count, and between BAS reward responsiveness scores and fixation duration. Findings led to the identification of a two-way process, where both the complexity of the tech-interaction and subjects’ personality traits are important impacting factors on the user’s visual exploration behavior. This research contributes to understand the user responsiveness adding first insights that may help to create more human-centered technology.

scholarly journals Automated Filtering of Eye Movements Using Dynamic AOI in Multiple Granularity Levels

2021 ◽  
Vol 12 (1) ◽  
pp. 49-64
Author(s):  
Gavindya Jayawardena ◽  
Sampath Jayarathna
Keyword(s):  
Eye Movements ◽  
Eye Tracking ◽  
Eye Movement ◽  
Eye Gaze ◽  
Movement Analysis ◽  
Movement Data ◽  
Bounding Boxes ◽  
Multiple Granularity ◽  
Instance Segmentation ◽  
Segmentation Models

Eye-tracking experiments involve areas of interest (AOIs) for the analysis of eye gaze data. While there are tools to delineate AOIs to extract eye movement data, they may require users to manually draw boundaries of AOIs on eye tracking stimuli or use markers to define AOIs. This paper introduces two novel techniques to dynamically filter eye movement data from AOIs for the analysis of eye metrics from multiple levels of granularity. The authors incorporate pre-trained object detectors and object instance segmentation models for offline detection of dynamic AOIs in video streams. This research presents the implementation and evaluation of object detectors and object instance segmentation models to find the best model to be integrated in a real-time eye movement analysis pipeline. The authors filter gaze data that falls within the polygonal boundaries of detected dynamic AOIs and apply object detector to find bounding-boxes in a public dataset. The results indicate that the dynamic AOIs generated by object detectors capture 60% of eye movements & object instance segmentation models capture 30% of eye movements.

Eye-Tracking Technology

2017 ◽  
pp. 283-302
Author(s):  
Chandni Parikh
Keyword(s):  
Eye Movements ◽  
Eye Tracking ◽  
Eye Gaze ◽  
Autism Spectrum ◽  
Gaze Direction ◽  
Red Flags ◽  
High Risk Infants ◽  
Behavioral Markers ◽  
Tracking Technology ◽  
Insight Into

Eye movements and gaze direction have been utilized to make inferences about perception and cognition since the 1800s. The driving factor behind recording overt eye movements stem from the fundamental idea that one's gaze provides tremendous insight into the information processing that takes place early on during development. One of the key deficits seen in individuals diagnosed with Autism Spectrum Disorders (ASD) involves eye gaze and social attention processing. The current chapter focuses on the use of eye-tracking technology with high-risk infants who are siblings of children diagnosed with ASD in order to highlight potential bio-behavioral markers that can inform the ascertainment of red flags and atypical behaviors associated with ASD within the first few years of development.

Evaluations That Matter: Customer Preferences Using Industry-Based Evaluations and Eye-Gaze Data

2016 ◽  
Author(s):  
Priya Seshadri ◽  
Youyi Bi ◽  
Jaykishan Bhatia ◽  
Ross Simons ◽  
Jeffrey Hartley ◽  
...  
Keyword(s):  
Eye Tracking ◽  
Facial Expressions ◽  
Research Program ◽  
Galvanic Skin Response ◽  
Eye Gaze ◽  
Fixation Time ◽  
Customer Preferences ◽  
Skin Response ◽  
Stimulus Format ◽  
2D And 3D

This study is the first stage of a research program aimed at understanding differences in how people process 2D and 3D automotive stimuli, using psychophysiological tools such as galvanic skin response (GSR), eye tracking, electroencephalography (EEG), and facial expressions coding, along with respondent ratings. The current study uses just one measure, eye tracking, and one stimulus format, 2D realistic renderings of vehicles, to reveal where people expect to find information about brand and other industry-relevant topics, such as sportiness. The eye-gaze data showed differences in the percentage of fixation time that people spent on different views of cars while evaluating the “Brand” and the degree to which they looked “Sporty/Conservative”, “Calm/Exciting”, and “Basic/Luxurious”. The results of this work can give designers insights on where they can invest their design efforts when considering brand and styling cues.

Sign in / Sign up

Export Citation Format

Share Document