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Byrne SA, Nyström M, Maquiling V, Kasneci E, Niehorster DC. Precise localization of corneal reflections in eye images using deep learning trained on synthetic data. Behav Res Methods 2024; 56:3226-3241. [PMID: 38114880 PMCID: PMC11133043 DOI: 10.3758/s13428-023-02297-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/15/2023] [Indexed: 12/21/2023]
Abstract
We present a deep learning method for accurately localizing the center of a single corneal reflection (CR) in an eye image. Unlike previous approaches, we use a convolutional neural network (CNN) that was trained solely using synthetic data. Using only synthetic data has the benefit of completely sidestepping the time-consuming process of manual annotation that is required for supervised training on real eye images. To systematically evaluate the accuracy of our method, we first tested it on images with synthetic CRs placed on different backgrounds and embedded in varying levels of noise. Second, we tested the method on two datasets consisting of high-quality videos captured from real eyes. Our method outperformed state-of-the-art algorithmic methods on real eye images with a 3-41.5% reduction in terms of spatial precision across data sets, and performed on par with state-of-the-art on synthetic images in terms of spatial accuracy. We conclude that our method provides a precise method for CR center localization and provides a solution to the data availability problem, which is one of the important common roadblocks in the development of deep learning models for gaze estimation. Due to the superior CR center localization and ease of application, our method has the potential to improve the accuracy and precision of CR-based eye trackers.
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Affiliation(s)
| | - Marcus Nyström
- Lund University Humanities Lab, Lund University, Lund, Sweden
| | - Virmarie Maquiling
- Human-Centered Technologies for Learning, Technical University of Munich, Munich, Germany
| | - Enkelejda Kasneci
- Human-Centered Technologies for Learning, Technical University of Munich, Munich, Germany
| | - Diederick C Niehorster
- MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy.
- Department of Psychology, Lund University, Lund, Sweden.
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Niehorster DC, Hessels RS, Benjamins JS, Nyström M, Hooge ITC. GlassesValidator: A data quality tool for eye tracking glasses. Behav Res Methods 2024; 56:1476-1484. [PMID: 37326770 PMCID: PMC10991001 DOI: 10.3758/s13428-023-02105-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2023] [Indexed: 06/17/2023]
Abstract
According to the proposal for a minimum reporting guideline for an eye tracking study by Holmqvist et al. (2022), the accuracy (in degrees) of eye tracking data should be reported. Currently, there is no easy way to determine accuracy for wearable eye tracking recordings. To enable determining the accuracy quickly and easily, we have produced a simple validation procedure using a printable poster and accompanying Python software. We tested the poster and procedure with 61 participants using one wearable eye tracker. In addition, the software was tested with six different wearable eye trackers. We found that the validation procedure can be administered within a minute per participant and provides measures of accuracy and precision. Calculating the eye-tracking data quality measures can be done offline on a simple computer and requires no advanced computer skills.
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Affiliation(s)
- Diederick C Niehorster
- Lund University Humanities Lab and Department of Psychology, Lund University, Lund, Sweden.
| | - Roy S Hessels
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, Netherlands
| | - Jeroen S Benjamins
- Experimental Psychology, Helmholtz Institute & Social, Health and Organisational Psychology, Utrecht University, Utrecht, Netherlands
| | - Marcus Nyström
- Lund University Humanities Lab, Lund University, Lund, Sweden
| | - Ignace T C Hooge
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, Netherlands
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Holmqvist K, Örbom SL, Hooge ITC, Niehorster DC, Alexander RG, Andersson R, Benjamins JS, Blignaut P, Brouwer AM, Chuang LL, Dalrymple KA, Drieghe D, Dunn MJ, Ettinger U, Fiedler S, Foulsham T, van der Geest JN, Hansen DW, Hutton SB, Kasneci E, Kingstone A, Knox PC, Kok EM, Lee H, Lee JY, Leppänen JM, Macknik S, Majaranta P, Martinez-Conde S, Nuthmann A, Nyström M, Orquin JL, Otero-Millan J, Park SY, Popelka S, Proudlock F, Renkewitz F, Roorda A, Schulte-Mecklenbeck M, Sharif B, Shic F, Shovman M, Thomas MG, Venrooij W, Zemblys R, Hessels RS. Eye tracking: empirical foundations for a minimal reporting guideline. Behav Res Methods 2023; 55:364-416. [PMID: 35384605 PMCID: PMC9535040 DOI: 10.3758/s13428-021-01762-8] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/29/2021] [Indexed: 11/08/2022]
Abstract
In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").
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Affiliation(s)
- Kenneth Holmqvist
- Department of Psychology, Nicolaus Copernicus University, Torun, Poland.
- Department of Computer Science and Informatics, University of the Free State, Bloemfontein, South Africa.
- Department of Psychology, Regensburg University, Regensburg, Germany.
| | - Saga Lee Örbom
- Department of Psychology, Regensburg University, Regensburg, Germany
| | - Ignace T C Hooge
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
| | - Diederick C Niehorster
- Lund University Humanities Lab and Department of Psychology, Lund University, Lund, Sweden
| | - Robert G Alexander
- Department of Ophthalmology, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | | | - Jeroen S Benjamins
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
- Social, Health and Organizational Psychology, Utrecht University, Utrecht, The Netherlands
| | - Pieter Blignaut
- Department of Computer Science and Informatics, University of the Free State, Bloemfontein, South Africa
| | | | - Lewis L Chuang
- Department of Ergonomics, Leibniz Institute for Working Environments and Human Factors, Dortmund, Germany
- Institute of Informatics, LMU Munich, Munich, Germany
| | | | - Denis Drieghe
- School of Psychology, University of Southampton, Southampton, UK
| | - Matt J Dunn
- School of Optometry and Vision Sciences, Cardiff University, Cardiff, UK
| | | | - Susann Fiedler
- Vienna University of Economics and Business, Vienna, Austria
| | - Tom Foulsham
- Department of Psychology, University of Essex, Essex, UK
| | | | - Dan Witzner Hansen
- Machine Learning Group, Department of Computer Science, IT University of Copenhagen, Copenhagen, Denmark
| | | | - Enkelejda Kasneci
- Human-Computer Interaction, University of Tübingen, Tübingen, Germany
| | | | - Paul C Knox
- Department of Eye and Vision Science, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Ellen M Kok
- Department of Education and Pedagogy, Division Education, Faculty of Social and Behavioral Sciences, Utrecht University, Utrecht, The Netherlands
- Department of Online Learning and Instruction, Faculty of Educational Sciences, Open University of the Netherlands, Heerlen, The Netherlands
| | - Helena Lee
- University of Southampton, Southampton, UK
| | - Joy Yeonjoo Lee
- School of Health Professions Education, Faculty of Health, Medicine, and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Jukka M Leppänen
- Department of Psychology and Speed-Language Pathology, University of Turku, Turku, Finland
| | - Stephen Macknik
- Department of Ophthalmology, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Päivi Majaranta
- TAUCHI Research Center, Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
| | - Susana Martinez-Conde
- Department of Ophthalmology, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Antje Nuthmann
- Institute of Psychology, University of Kiel, Kiel, Germany
| | - Marcus Nyström
- Lund University Humanities Lab, Lund University, Lund, Sweden
| | - Jacob L Orquin
- Department of Management, Aarhus University, Aarhus, Denmark
- Center for Research in Marketing and Consumer Psychology, Reykjavik University, Reykjavik, Iceland
| | - Jorge Otero-Millan
- Herbert Wertheim School of Optometry and Vision Science, University of California, Berkeley, CA, USA
| | - Soon Young Park
- Comparative Cognition, Messerli Research Institute, University of Veterinary Medicine Vienna, Medical University of Vienna, Vienna, Austria
| | - Stanislav Popelka
- Department of Geoinformatics, Palacký University Olomouc, Olomouc, Czech Republic
| | - Frank Proudlock
- The University of Leicester Ulverscroft Eye Unit, Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
| | - Frank Renkewitz
- Department of Psychology, University of Erfurt, Erfurt, Germany
| | - Austin Roorda
- Herbert Wertheim School of Optometry and Vision Science, University of California, Berkeley, CA, USA
| | | | - Bonita Sharif
- School of Computing, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Frederick Shic
- Center for Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle, WA, USA
- Department of General Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
| | - Mark Shovman
- Eyeviation Systems, Herzliya, Israel
- Department of Industrial Design, Bezalel Academy of Arts and Design, Jerusalem, Israel
| | - Mervyn G Thomas
- The University of Leicester Ulverscroft Eye Unit, Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
| | - Ward Venrooij
- Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, The Netherlands
| | | | - Roy S Hessels
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
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Holmqvist K, Örbom SL, Zemblys R. Small head movements increase and colour noise in data from five video-based P-CR eye trackers. Behav Res Methods 2022; 54:845-863. [PMID: 34357538 PMCID: PMC8344338 DOI: 10.3758/s13428-021-01648-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/06/2021] [Indexed: 11/08/2022]
Abstract
We empirically investigate the role of small, almost imperceptible balance and breathing movements of the head on the level and colour of noise in data from five commercial video-based P-CR eye trackers. By comparing noise from recordings with completely static artificial eyes to noise from recordings where the artificial eyes are worn by humans, we show that very small head movements increase levels and colouring of the noise in data recorded from all five eye trackers in this study. This increase of noise levels is seen not only in the gaze signal, but also in the P and CR signals of the eye trackers that provide these camera image features. The P and CR signals of the SMI eye trackers correlate strongly during small head movements, but less so or not at all when the head is completely still, indicating that head movements are registered by the P and CR images in the eye camera. By recording with artificial eyes, we can also show that the pupil size artefact has no major role in increasing and colouring noise. Our findings add to and replicate the observation by Niehorster et al., (2021) that lowpass filters in video-based P-CR eye trackers colour the data. Irrespective of source, filters or head movements, coloured noise can be confused for oculomotor drift. We also find that usage of the default head restriction in the EyeLink 1000+, the EyeLink II and the HiSpeed240 result in noisier data compared to less head restriction. Researchers investigating data quality in eye trackers should consider not using the Gen 2 artificial eye from SR Research / EyeLink. Data recorded with this artificial eye are much noisier than data recorded with other artificial eyes, on average 2.2-14.5 times worse for the five eye trackers.
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Affiliation(s)
- Kenneth Holmqvist
- Institute of Psychology, Nicolaus Copernicus University in Torun, Torun, Poland
- Department of Psychology, Regensburg University, Regensburg, Germany
- Department of Computer Science and Informatics, University of the Free State, Bloemfontein, South Africa
| | - Saga Lee Örbom
- Department of Psychology, Regensburg University, Regensburg, Germany
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