Evidence map›Paper›PMID 34372344›Full record

ArticleSensors (Basel, Switzerland)2021

Low-Cost Eye Tracking Calibration: A Knowledge-Based Study.

Gonzalo Garde, Andoni Larumbe-Bergera, Benoît Bossavit, Sonia Porta, Rafael Cabeza, Arantxa Villanueva

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Gonzalo GardeDepartment of Electrical, Electronic and Communications Engineering, Arrosadia Campus, Public University of Navarre, 31006 Pamplona, Spain.ORCID 0000-0002-3635-6530
Andoni Larumbe-BergeraDepartment of Electrical, Electronic and Communications Engineering, Arrosadia Campus, Public University of Navarre, 31006 Pamplona, Spain.ORCID 0000-0002-8075-2426
Benoît BossavitSchool of Computer Science and Statistics, Trinity College Dublin, The University of Dublin, College Green, D02 PN40 Dublin 2, Ireland.ORCID 0000-0003-1236-3020
Sonia PortaDepartment of Electrical, Electronic and Communications Engineering, Arrosadia Campus, Public University of Navarre, 31006 Pamplona, Spain.ORCID 0000-0002-3687-4477
Rafael CabezaDepartment of Electrical, Electronic and Communications Engineering, Arrosadia Campus, Public University of Navarre, 31006 Pamplona, Spain.ORCID 0000-0001-7999-1182
Arantxa VillanuevaDepartment of Electrical, Electronic and Communications Engineering, Arrosadia Campus, Public University of Navarre, 31006 Pamplona, Spain.ORCID 0000-0001-9822-2530

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Subject calibration has been demonstrated to improve the accuracy in high-performance eye trackers. However, the true weight of calibration in off-the-shelf eye tracking solutions is still not addressed. In this work, a theoretical framework to measure the effects of calibration in deep learning-based gaze estimation is proposed for low-resolution systems. To this end, features extracted from the synthetic U2Eyes dataset are used in a fully connected network in order to isolate the effect of specific user's features, such as kappa angles. Then, the impact of system calibration in a real setup employing I2Head dataset images is studied. The obtained results show accuracy improvements over 50%, probing that calibration is a key process also in low-resolution gaze estimation scenarios. Furthermore, we show that after calibration accuracy values close to those obtained by high-resolution systems, in the range of 0.7°, could be theoretically obtained if a careful selection of image features was performed, demonstrating significant room for improvement for off-the-shelf eye tracking systems.

Indexed as

Eye-Tracking TechnologyFixation, OcularCalibrationcalibrationgaze-estimationlow-resolutiontheoretical analysis

Identifiers

PMID34372344
PMCPMC8347006

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.