Appearance-based gaze estimation using kinect

Choi, Jinsoo; Ahn, Byung-Tae; Parl, Jaesik; Kweon, In So

doi:10.1109/urai.2013.6677362

Cited by 10 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Appearance-based gaze estimation methods directly use eye images as input and can therefore potentially work with low-resolution eye images. While early works assumed a fixed head pose [3,42,48,35,27,24], recent works focused on methods for 3D head pose estimation [25,26,9,6]. However, appearance-based methods require larger amounts of user-specific training data than model-based methods, and it remains unclear if the learned estimator can generalise to unknown users.…”

Section: Gaze Estimation Methodsmentioning

confidence: 99%

Appearance-based gaze estimation in the wild

Zhang

Sugano

Fritz

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

680

630

View full text Add to dashboard Cite

Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art imagebased gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild.

show abstract

Section: Gaze Estimation Methodsmentioning

confidence: 99%

Appearance-based gaze estimation in the wild

Zhang

Sugano

Fritz

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

680

630

View full text Add to dashboard Cite

show abstract

“…In contrast, appearance-based gaze estimation methods do not rely on feature point detection but directly regress from eye images to 3D gaze directions. While early methods assumed a fixed head pose [37], [38], [39], [40], [41], [42], more recent methods allow for free 3D head movement in front of the camera [43], [44], [45], [46], [47]. Because they do not rely on any explicit shape extraction stage, appearance-based methods can handle lowresolution images and long distances.…”

Section: Gaze Estimation Methodsmentioning

confidence: 99%

MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation

Zhang

Sugano

Fritz

et al. 2019

IEEE Trans. Pattern Anal. Mach. Intell.

393

378

View full text Add to dashboard Cite

Abstract-Learning-based methods are believed to work well for unconstrained gaze estimation, i.e. gaze estimation from a monocular RGB camera without assumptions regarding user, environment, or camera. However, current gaze datasets were collected under laboratory conditions and methods were not evaluated across multiple datasets. Our work makes three contributions towards addressing these limitations. First, we present the MPIIGaze dataset, which contains 213,659 full face images and corresponding ground-truth gaze positions collected from 15 users during everyday laptop use over several months. An experience sampling approach ensured continuous gaze and head poses and realistic variation in eye appearance and illumination. To facilitate cross-dataset evaluations, 37,667 images were manually annotated with eye corners, mouth corners, and pupil centres. Second, we present an extensive evaluation of state-of-the-art gaze estimation methods on three current datasets, including MPIIGaze. We study key challenges including target gaze range, illumination conditions, and facial appearance variation. We show that image resolution and the use of both eyes affect gaze estimation performance, while head pose and pupil centre information are less informative. Finally, we propose GazeNet, the first deep appearance-based gaze estimation method. GazeNet improves on the state of the art by 22% (from a mean error of 13.9 degrees to 10.8 degrees) for the most challenging cross-dataset evaluation.

show abstract

“…To detect gaze points without fixing the head, the depth images of the Kinect can be used [18]. Choi [19] used a Kinect to obtain eye regions and head posture. The ASM [20] and KLT [21] algorithms were used to extract eye appearances for the eye vector and Gaussian regression was used to map the eye vectors and screen points.…”

Section: Introductionmentioning

confidence: 99%

Pupil Detection Based on Oblique Projection Using a Binocular Camera

et al. 2020

View full text Add to dashboard Cite

The real visual attention areas of students during the learning process are vital data. They represent the students' visual concentration and provide strong support for the analysis of learning effects. It is necessary to use professional equipment to collect the visual attention areas of students on a screen but this equipment is expensive, so it cannot be solely depended upon. A binocular camera can directly obtain the depth information under static conditions; thus, detected pupil and iris sizes do not change as the user-toscreen distance changes. Robust pupil detection is an important prerequisite for gaze detection in a real-world setting. Hence, an accurate algorithm to calculate the position of pupils and the size of irises under natural light is proposed in this paper. It is based on the positive and negative oblique projection of relative linear density. The MPIIGaze dataset was used to test the algorithm, and the experimental results show that the algorithm is resistant to changes in illumination and the presence of glasses. Moreover, compared with a system based on a monocular camera, the error in the distance between label points and gaze location is decreased by 5 pixels and dispersion is decreased by 3 pixels. Finally, the line of sight is concentrated and the dispersion is low. With the 'user-screen' distance between 60cm and 80cm, the accuracy can reach up to 1.5° to 2.2°.

show abstract

Appearance-based gaze estimation using kinect

Cited by 10 publications

References 4 publications

Appearance-based gaze estimation in the wild

Appearance-based gaze estimation in the wild

MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation

Pupil Detection Based on Oblique Projection Using a Binocular Camera

Contact Info

Product

Resources

About