CNN-Based Pupil Center Detection for Wearable Gaze Estimation System

Chinsatit, Warapon; Saitoh, Tohru

doi:10.1155/2017/8718956

Cited by 37 publications

(10 citation statements)

References 14 publications

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“…Some other researchers applied convolutional neural networks for regression and prediction problems i.e. human eye fixations and pose estimation [67, 68].…”

Section: Resultsmentioning

confidence: 99%

Use of information modelling techniques to understand research trends in eye gaze estimation methods: An automated review

Singh

Modi

2019

Heliyon

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Eye gaze tracking has been used to study the influence of visual stimuli on consumer behavior and attentional processes. Eye gaze tracking techniques have made substantial contributions in advertisement design, human computer interaction, virtual reality and disease diagnosis. Eye gaze estimation is considered critical for prediction of human attention, and hence indispensable for better understanding human activities. In this paper, Latent Semantic Analysis is used to develop an information model for identifying emerging research trends within eye gaze estimation techniques. An exhaustive collection of 423 titles and abstracts of research papers published during 2005–2018 were used. Five major research areas and ten research trends were classified based upon this study.

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“…Some other researchers applied convolutional neural networks for regression and prediction problems i.e. human eye fixations and pose estimation [67, 68].…”

Section: Resultsmentioning

confidence: 99%

Use of information modelling techniques to understand research trends in eye gaze estimation methods: An automated review

Singh

Modi

2019

Heliyon

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“…Considering the performance gains brought by CNNs in many object tracking and detection problems, there have also been several methods to apply the CNN to the pupil detection. For example, like recent object detectors that find bounding box over an object of interest, Chinsatit and Saitoh [14] used the CNN-based regression method. Specifically, they take the eye image as the input to the CNN, which directly produces the pupil center as a regression result.…”

Section: B Learning-based Pupil Detection Algorithmsmentioning

confidence: 99%

“…With the remarkable development of deep learning methods, there have also been several attempts to apply CNNs for pupil detection. For some examples, Fuhl et al [13], Chinsatit and Saitoh [14], and Kondo et al [15] proposed CNNs that take the eye image as the input and directly generate the gaze point. They showed improved results than the traditional approaches to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

Noise-Robust Pupil Center Detection Through CNN-Based Segmentation With Shape-Prior Loss

et al. 2020

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Detecting the pupil center plays a key role in human-computer interaction, especially for gaze tracking. The conventional deep learning-based method for this problem is to train a convolutional neural network (CNN), which takes the eye image as the input and gives the pupil center as a regression result. In this paper, we propose an indirect use of the CNN for the task, which first segments the pupil region by a CNN as a classification problem, and then finds the center of the segmented region. This is based on the observation that CNN works more robustly for the pupil segmentation than for the pupil center-point regression when the inputs are noisy IR images. Specifically, we use the UNet model for the segmentation of pupil regions in IR images and then find the pupil center as the center of mass of the segment. In designing the loss function for the segmentation, we propose a new loss term that encodes the convex shape-prior for enhancing the robustness to noise. Precisely, we penalize not only the deviation of each predicted pixel from the ground truth label but also the non-convex shape of pupils caused by the noise and reflection. For the training, we make a new dataset of 111,581 images with hand-labeled pupil regions from 29 IR eye video sequences. We also label commonly used datasets (ExCuSe and ElSe dataset) that are considered real-world noisy ones to validate our method. Experiments show that the proposed method performs better than the conventional methods that directly find the pupil center as a regression result. INDEX TERMS Convex shape prior, deep learning, pupil segmentation, U-Net.

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“…With the popularity of deep learning algorithms [17], some researchers used the convolution neural network to train eye detectors, which forms the second subclass. Chinsatit and Saitoh [18] present a CNN-based pupil center detection method. In Fuhl's [19] research, coarse to fine pupil position identification was carried out using two similar convolutional neural networks and the authors proposed subregions from a downscaled input image to decrease computational costs.…”

Section: Related Workmentioning

confidence: 99%

Real Time Eye Detector with Cascaded Convolutional Neural Networks

Hong

2018

Applied Computational Intelligence and Soft Computing

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An accurate and efficient eye detector is essential for many computer vision applications. In this paper, we present an efficient method to evaluate the eye location from facial images. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs) is adopted to determine the most likely eye region and classify the region as left or right eye; finally, the center of the eye is located with other CNNs. In the experiments using GI4E, BioID, and our datasets, our method attained a detection accuracy which is comparable to existing state-of-the-art methods; meanwhile, our method was faster and adaptable to variations of the images, including external light changes, facial occlusion, and changes in image modality.

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CNN-Based Pupil Center Detection for Wearable Gaze Estimation System

Cited by 37 publications

References 14 publications

Use of information modelling techniques to understand research trends in eye gaze estimation methods: An automated review

Use of information modelling techniques to understand research trends in eye gaze estimation methods: An automated review

Noise-Robust Pupil Center Detection Through CNN-Based Segmentation With Shape-Prior Loss

Real Time Eye Detector with Cascaded Convolutional Neural Networks

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