Appearance-based gaze estimation in the wild

Zhang, Xucong; Sugano, Yusuke; Fritz, Mario; Bulling, Andreas

doi:10.1109/cvpr.2015.7299081

Cited by 661 publications

(702 citation statements)

References 42 publications

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“…Zhang et al proposed a CNNbased gaze estimation method in an unconstrained daily life setting [8]. In that method, the input data are an eye image and the 2D head angle, and the output is a 2D gaze angle vector that consists of two gaze angles, that is, yaw and pitch.…”

Section: Related Researchmentioning

confidence: 99%

“…There are some public datasets of eye images [8,9]; however, such datasets do not typically include images of eyes in the blink state. We describe a process to capture a sufficiently large image dataset with good distribution and variety of pupil position and eye state.…”

Section: Applied Computational Intelligence and Soft Computingmentioning

confidence: 99%

“…Moreover, a variety of ground truths are required to make the model more accurate. MPIIGaze [8] is a well-known eye image dataset composed of medial canthus, lateral canthus, and pupil points. However, the pupil points are not center points.…”

Section: Datasetmentioning

confidence: 99%

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CNN-Based Pupil Center Detection for Wearable Gaze Estimation System

Chinsatit

Saitoh

2017

Applied Computational Intelligence and Soft Computing

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This paper presents a convolutional neural network-(CNN-) based pupil center detection method for a wearable gaze estimation system using infrared eye images. Potentially, the pupil center position of a user's eye can be used in various applications, such as human-computer interaction, medical diagnosis, and psychological studies. However, users tend to blink frequently; thus, estimating gaze direction is difficult. The proposed method uses two CNN models. The first CNN model is used to classify the eye state and the second is used to estimate the pupil center position. The classification model filters images with closed eyes and terminates the gaze estimation process when the input image shows a closed eye. In addition, this paper presents a process to create an eye image dataset using a wearable camera. This dataset, which was used to evaluate the proposed method, has approximately 20,000 images and a wide variation of eye states. We evaluated the proposed method from various perspectives. The result shows that the proposed method obtained good accuracy and has the potential for application in wearable device-based gaze estimation.

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Section: Related Researchmentioning

confidence: 99%

Section: Applied Computational Intelligence and Soft Computingmentioning

confidence: 99%

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CNN-Based Pupil Center Detection for Wearable Gaze Estimation System

Chinsatit

Saitoh

2017

Applied Computational Intelligence and Soft Computing

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“…A Machine Learning framework is proposed to classify the gaze direction for Iris, sclera, and skin. Additionally, gaze estimation is calculated by using Convolutional Neural Network (CNN) [15]. They combined face pose estimator's data with eye region.…”

Section: Related Workmentioning

confidence: 99%

Effect of DCT Image Compression on Eye Gaze Direction Detection

et al. 2018

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Abstract. The prediction of eye direction detection is the one of the popular research topic in human computer interaction area. This paper defines eye gaze detection by using Discrete Cosine Transform. Actually, determining the position of eyes is difficult to estimate the location of gaze which is more challenging. The database of the suggested research is organized as gaze directions of right, left and centre. The database is created with varied ages of images. In this paper, Discrete Cosine Transform has been applied to the image database and the effect of image compression is tested by using back propagation neural networks.

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“…An area that has received a large amount of recent interest is gaze-tracking using appearance-based models [12], [6], [5], [13]. Appearance-based gaze tracking enables gaze gesture analysis by performing gaze tracking on standard monocular video signals.…”

Section: Previous Work On Gaze Aversionmentioning

confidence: 99%

Detecting conversational gaze aversion using unsupervised learning

Roddy

Harte

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The aversion of gaze during dyadic conversations is a social signal that contains information relevant to the detection of interest, turn-taking cues, and conversational engagement. The understanding and modeling of such behavior has implications for the design of embodied conversational agents, as well as computational approaches to conversational analysis. Recent approaches to extracting gaze directions from monocular camera footage have achieved accurate results. We investigate ways of processing the extracted gaze signals from videos to perform gaze aversion detection. We present novel approaches that are based on unsupervised classification using spectral clustering as well as optimization methods. Three approaches that vary in their input parameters and their complexity are proposed and evaluated.

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Appearance-based gaze estimation in the wild

Cited by 661 publications

References 42 publications

CNN-Based Pupil Center Detection for Wearable Gaze Estimation System

CNN-Based Pupil Center Detection for Wearable Gaze Estimation System

Effect of DCT Image Compression on Eye Gaze Direction Detection

Detecting conversational gaze aversion using unsupervised learning

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