Deep Learning-Based Pupil Center Detection for Fast and Accurate Eye Tracking System

Lee, Kang Il; Jeon, Jung Ho; Song, Byung Cheol

doi:10.1007/978-3-030-58529-7_3

Cited by 22 publications

(10 citation statements)

References 22 publications

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“…The proposed approach outperformed the previously proposed approaches on public datasets, such as BioID and GI4E. A robust alternative to wearing glasses was proposed by Lee et al [8]. It utilized appearance-based pupil center estimation inspired by the work of Choi et al [6].…”

Section: -Introductionmentioning

confidence: 95%

A YOLO Detector Providing Fast and Accurate Pupil Center Estimation using Regions Surrounding a Pupil

Kurdthongmee

Suwannarat

et al. 2022

Emerg Sci J

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Eye-tracking technology has many useful applications, including Virtual Reality (VR) devices, Augmented Reality (AR) devices, and assistive technology. The main objective of eye-tracking technology is to detect eye position and track eye movements. It is possible to determine the eye position when the pupil center is detected. In this paper, a deep learning-based approach to the detection of pupil centers from webcam images is presented. As opposed to all previous approaches to object detection based on training the detector with objects to be detected, our object detector was trained with both the region surrounding a pupil and the region between an eye and the region surrounding a pupil. The latter set of regions has been found to increase the overall detection accuracy. A novel post-processing algorithm is also presented to estimate the pupil center from all the detected regions. To achieve real-time performance, we have adopted the tiny architecture of YOLOv3, which has 23 layers and can be executed without requiring a GPU accelerator. To train the detectors, different variations of regions covering a pupil and an eye were used, as well as expanding regions surrounding a pupil and an eye. The PUPPIE dataset was used as the primary input for training the detector. The setting with the best detection accuracy was applied to all publicly available datasets: I2Head, MPIIGaze, and U2Eyes. In terms of accuracy, the results indicate that pupil center estimation is comparable to the state-of-the-art approach. It achieves pupil center estimation errors below the size of a constricted pupil in more than 98.24% of images. Furthermore, the detection time is 2.8 times faster than the state-of-the-art approach. Doi: 10.28991/ESJ-2022-06-05-05 Full Text: PDF

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Section: -Introductionmentioning

confidence: 95%

A YOLO Detector Providing Fast and Accurate Pupil Center Estimation using Regions Surrounding a Pupil

Kurdthongmee

Suwannarat

et al. 2022

Emerg Sci J

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“…Once the image is segmented, the pixel with the maximum intensity is determined as the pupil center. Another method robust against glasses wearing is the one proposed by Lee et al [ 23 ]. That consists of an appearance-based pupil center detection, inspired by [ 21 ] but employing perceptual loss to mitigate the blur phenomenon produced by the glass removal network, and mutual information maximization to enhance the representation quality of the segmentation network.…”

Section: Related Workmentioning

confidence: 99%

“…For this non-invasive webcam scenario, the relevance of learning and training methodologies begins to be more important and shows up as a promising tool [ 21 , 22 , 23 ]. These training-based techniques require a large amount of images representing the variability of the problem in order to get adapted to the solution and be able to generalize.…”

Section: Introductionmentioning

confidence: 99%

Accurate Pupil Center Detection in Off-the-Shelf Eye Tracking Systems Using Convolutional Neural Networks

Larumbe-Bergera

Garde

Porta

et al. 2021

Sensors

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Remote eye tracking technology has suffered an increasing growth in recent years due to its applicability in many research areas. In this paper, a video-oculography method based on convolutional neural networks (CNNs) for pupil center detection over webcam images is proposed. As the first contribution of this work and in order to train the model, a pupil center manual labeling procedure of a facial landmark dataset has been performed. The model has been tested over both real and synthetic databases and outperforms state-of-the-art methods, achieving pupil center estimation errors below the size of a constricted pupil in more than 95% of the images, while reducing computing time by a 8 factor. Results show the importance of use high quality training data and well-known architectures to achieve an outstanding performance.

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“…An approach that overcomes these drawbacks and minimizes or eliminates manual intervention is therefore required to reliably conduct experiments with rodents. In this regard, the application of DL was proven to be an effective approach for real-time pupil detection in humans [35][36][37][38][39]. These human studies are overwhelmingly oriented toward estimating pupil centers to determine gaze.…”

Section: Recent Related Workmentioning

confidence: 99%

Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach

Lara-Doña

Torres-Sánchez

Torres

et al. 2021

Sensors

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Strong evidence from studies on primates and rodents shows that changes in pupil diameter may reflect neural activity in the locus coeruleus (LC). Pupillometry is the only available non-invasive technique that could be used as a reliable and easily accessible real-time biomarker of changes in the in vivo activity of the LC. However, the application of pupillometry to preclinical research in rodents is not yet fully standardized. A lack of consensus on the technical specifications of some of the components used for image recording or positioning of the animal and cameras have been recorded in recent scientific literature. In this study, a novel pupillometry system to indirectly assess, in real-time, the function of the LC in anesthetized rodents is presented. The system comprises a deep learning SOLOv2 instance-based fast segmentation framework and a platform designed to place the experimental subject, the video cameras for data acquisition, and the light source. The performance of the proposed setup was assessed and compared to other baseline methods using a validation and an external test set. In the latter, the calculated intersection over the union was 0.93 and the mean absolute percentage error was 1.89% for the selected method. The Bland–Altman analysis depicted an excellent agreement. The results confirmed a high accuracy that makes the system suitable for real-time pupil size tracking, regardless of the pupil’s size, light intensity, or any features typical of the recording process in sedated mice. The framework could be used in any neurophysiological study with sedated or fixed-head animals.

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Deep Learning-Based Pupil Center Detection for Fast and Accurate Eye Tracking System

Cited by 22 publications

References 22 publications

A YOLO Detector Providing Fast and Accurate Pupil Center Estimation using Regions Surrounding a Pupil

A YOLO Detector Providing Fast and Accurate Pupil Center Estimation using Regions Surrounding a Pupil

Accurate Pupil Center Detection in Off-the-Shelf Eye Tracking Systems Using Convolutional Neural Networks

Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach

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