Personalization of Gaze Direction Estimation with Deep Learning

TźSér, Zoltán; Rill, Róbert Adrian; Faragó, Kinga Bettina; Jeni, László A.; LźRincz, András

doi:10.1007/978-3-319-46073-4_20

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…A separate category is that of gaze estimation, which was carried out in 13 studies [85][86][87][88][89][90][91][92][93][94][95][96][97].…”

Section: Applications Of Artificial Intelligence Enhanced Eye Trackingmentioning

confidence: 99%

“…Task Additional Parameters [64] attention estimation EEG, head movement [38] identifying children with ASD questionnaire, age, gender [56] predicting dwell time in a museum face expression, body movement, interaction trace logs [27] affect recognition EEG, ECG [8] predicting students' performance and effort EEG, face videos, arousal data from wristband [71] predicting take-over time head position, body posture, simulation data [30] predicting liking a video infrared thermal image, heart rate, face expression [32] predicting user confidence Time [25] predicting reaction to ads gender, age, survey, time, ad parameters, behavior connected with an ad (e.g., sharing) [13] predicting readability text features [17] predicting SAT score Time [36] predicting the emotion of an observed person EEG, empatica bracelet [32] predicting social plane of interaction EEG, accelerometer, audio, video [33] detecting user confusion mouse actions, distance of the user's head from the screen [72] predicting mental workload Reaction time [42] detecting people with dyslexia age, text characteristics [74] predicting reduced driver alertness EEG [19] predicting learning curve perceptual speed, verbal working memory, visual working memory, locus of control [37] classifying emotions in pictures image [89] predicting eye movement distance between the object and the distractor [41] predicting Parkinson symptoms' development age, sex, duration of the disease [23] emotion estimation head movement, body movement, audio, video of the face…”

Section: Refmentioning

confidence: 99%

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What Is Hidden in Clear Sight and How to Find It—A Survey of the Integration of Artificial Intelligence and Eye Tracking

Kędras,

Sobecki

2023

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This paper presents an overview of the uses of the combination of eye tracking and artificial intelligence. In the paper, several aspects of both eye tracking and applied AI methods have been analyzed. It analyzes the eye tracking hardware used along with the sampling frequency, the number of test participants, additional parameters, the extraction of features, the artificial intelligence methods used and the methods of verification of the results. Finally, it includes a comparison of the results obtained in the analyzed literature and a discussion about them.

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“…A separate category is that of gaze estimation, which was carried out in 13 studies [85][86][87][88][89][90][91][92][93][94][95][96][97].…”

Section: Applications Of Artificial Intelligence Enhanced Eye Trackingmentioning

confidence: 99%

Section: Refmentioning

confidence: 99%

What Is Hidden in Clear Sight and How to Find It—A Survey of the Integration of Artificial Intelligence and Eye Tracking

Kędras,

Sobecki

2023

Information

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“…A straightforward solution to this problem is to learn person-specific models [10], [19], [20] or fine-tune a pretrained model [21]. Note that even regular high-end Infra-Red (IR) devices (eg from Tobii) require users to stare at several fixed positions before using them.…”

mentioning

confidence: 99%

A Differential Approach for Gaze Estimation

Liu

Mora

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

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Most non-invasive gaze estimation methods regress gaze directions directly from a single face or eye image. However, due to important variabilities in eye shapes and inner eye structures amongst individuals, universal models obtain limited accuracies and their output usually exhibit high variance as well as subject dependent biases. Thus, increasing accuracy is usually done through calibration, allowing gaze predictions for a subject to be mapped to her actual gaze. In this paper, we introduce a novel approach, which works by directly training a differential convolutional neural network to predict gaze differences between two eye input images of the same subject. Then, given a set of subject specific calibration images, we can use the inferred differences to predict the gaze direction of a novel eye sample. The assumption is that by comparing eye images of the same user, annoyance factors (alignment, eyelid closing, illumination perturbations) which usually plague single image prediction methods can be much reduced, allowing better prediction altogether. Furthermore, the differential network itself can be adapted via finetuning to make predictions consistent with the available user reference pairs. Experiments on 3 public datasets validate our approach which constantly outperforms state-of-the-art methods even when using only one calibration sample or those relying on subject specific gaze adaptation.

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A review on visible-light eye-tracking methods based on a low-cost camera

Molina-Cantero,

Lebrato-Vázquez,

Castro-García

et al. 2024

J Ambient Intell Human Comput

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This paper is the first of a two-part study aiming at building a low-cost visible-light eye tracker (ET) for people with amyotrophic lateral sclerosis (ALS). The whole study comprises several phases: (1) analysis of the scientific literature, (2) selection of the studies that better fit the main goal, (3) building the ET, and (4) testing with final users. This document basically contains the two first phases, in which more than 500 studies, from different scientific databases (IEEE Xplore, Scopus, SpringerLink, etc.), fulfilled the inclusion criteria, and were analyzed following the guidelines of a scoping review. Two researchers screened the searching results and selected 44 studies (-value = 0.86, Kappa Statistic). Three main methods (appearance-, feature- or model- based) were identified for visible-light ETs, but none significantly outperformed the others according to the reported accuracy -p = 0.14, Kruskal–Wallis test (KW)-. The feature-based method is abundant in the literature, although the number of appearance-based studies is increasing due to the use of deep learning techniques. Head movements worsen the accuracy in ETs, and only a very few numbers of studies considered the use of algorithms to correct the head pose. Even though head movements seem not to be a big issue for people with ALS, some slight head movements might be enough to worsen the ET accuracy. For this reason, only studies that did not constrain the head movements with a chinrest were considered. Five studies fulfilled the selection criteria with accuracies less than $$2^{\circ }$$ 2 ∘ , and one of them is illuminance invariant.

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Personalization of Gaze Direction Estimation with Deep Learning

Cited by 5 publications

References 19 publications

What Is Hidden in Clear Sight and How to Find It—A Survey of the Integration of Artificial Intelligence and Eye Tracking

What Is Hidden in Clear Sight and How to Find It—A Survey of the Integration of Artificial Intelligence and Eye Tracking

A Differential Approach for Gaze Estimation

A review on visible-light eye-tracking methods based on a low-cost camera

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