Wearable eye tracker calibration at your fingertips

Bâce, Mihai; Staal, Sander; Sörös, Gábor

doi:10.1145/3204493.3204592

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…The authors achieved an average accuracy of 2.9°. In one work [ 14 ], users’ hands and fingertips were used as calibration samples. Users only had to point with their finger to various locations in the scene.…”

Section: Introductionmentioning

confidence: 99%

3D Gaze Estimation Using RGB-IR Cameras

Mokatren

Kuflik

Shimshoni

2022

Sensors

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In this paper, we present a framework for 3D gaze estimation intended to identify the user’s focus of attention in a corneal imaging system. The framework uses a headset that consists of three cameras, a scene camera and two eye cameras: an IR camera and an RGB camera. The IR camera is used to continuously and reliably track the pupil and the RGB camera is used to acquire corneal images of the same eye. Deep learning algorithms are trained to detect the pupil in IR and RGB images and to compute a per user 3D model of the eye in real time. Once the 3D model is built, the 3D gaze direction is computed starting from the eyeball center and passing through the pupil center to the outside world. This model can also be used to transform the pupil position detected in the IR image into its corresponding position in the RGB image and to detect the gaze direction in the corneal image. This technique circumvents the problem of pupil detection in RGB images, which is especially difficult and unreliable when the scene is reflected in the corneal images. In our approach, the auto-calibration process is transparent and unobtrusive. Users do not have to be instructed to look at specific objects to calibrate the eye tracker. They need only to act and gaze normally. The framework was evaluated in a user study in realistic settings and the results are promising. It achieved a very low 3D gaze error (2.12) and very high accuracy in acquiring corneal images (intersection over union—IoU = 0.71). The framework may be used in a variety of real-world mobile scenarios (indoors, indoors near windows and outdoors) with high accuracy.

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Section: Introductionmentioning

confidence: 99%

3D Gaze Estimation Using RGB-IR Cameras

Mokatren

Kuflik

Shimshoni

2022

Sensors

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“…The aim of this section of the indoor session was to collect data from the three cameras while participants tracked screen markers on a computer screen (see Figure 11 and Figure 12 ). This method is widely used to evaluate eye-tracking techniques [ 10 , 11 , 12 , 13 ]. The participants were asked to sit normally in front of the screen and track the screen marker.…”

Section: Experimentationmentioning

confidence: 99%

“…The authors attained an average accuracy of 2.9 • . In a separate study [12], users' hands and fingertips served as calibration samples, with users simply pointing at various locations in the scene. Although the proposed approach demonstrated accuracy comparable to marker-based calibration techniques, it still necessitated users to direct their gaze to specific points in the scene, achieving an average accuracy of 2.68 • .…”

Section: Introductionmentioning

confidence: 99%

Calibration-Free Mobile Eye-Tracking Using Corneal Imaging

Mokatren,

Kuflik,

Shimshoni

2024

Sensors

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In this paper, we present and evaluate a calibration-free mobile eye-traking system. The system’s mobile device consists of three cameras: an IR eye camera, an RGB eye camera, and a front-scene RGB camera. The three cameras build a reliable corneal imaging system that is used to estimate the user’s point of gaze continuously and reliably. The system auto-calibrates the device unobtrusively. Since the user is not required to follow any special instructions to calibrate the system, they can simply put on the eye tracker and start moving around using it. Deep learning algorithms together with 3D geometric computations were used to auto-calibrate the system per user. Once the model is built, a point-to-point transformation from the eye camera to the front camera is computed automatically by matching corneal and scene images, which allows the gaze point in the scene image to be estimated. The system was evaluated by users in real-life scenarios, indoors and outdoors. The average gaze error was 1.6∘ indoors and 1.69∘ outdoors, which is considered very good compared to state-of-the-art approaches.

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“…Sugano treats mouse clicks on a computer screen as gaze points to train the mapping function between the eye features and PoR [14]. Similar to [14], the algorithm in [15] detects the user's hand and fingertip which indicate the user's point of interest. This method can easily collect calibration samples in different environments quickly, and the proposed method achieves comparable accuracy to standard markerbased calibration.…”

Section: Introductionmentioning

confidence: 99%

3D Gaze Estimation for Head-Mounted Eye Tracking System With Auto-Calibration Method

Liu

2020

IEEE Access

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The general challenges of 3D gaze estimation for head-mounted eye tracking systems are inflexible marker-based calibration procedure and significant errors of depth estimation. In this paper, we propose a 3D gaze estimation with an auto-calibration method. To acquire the accurate 3D structure of the environment, an RGBD camera is applied as the scene camera of our system. By adopting the saliency detection method, saliency maps can be acquired through scene images, and 3D salient pixels in the scene are considered potential 3D calibration targets. The 3D eye model is built on the basis of eye images to determine gaze vectors. By combining 3D salient pixels and gaze vectors, the auto-calibration can be achieved with our calibration method. Finally, the 3D gaze point is obtained through the calibrated gaze vectors, and the point cloud is generated from the RGBD camera. The experimental result shows that the proposed system can achieve an average accuracy of 3.7 • in the range of 1 m to 4 m indoors and 4.0 • outdoors. The proposed system also presents a great improvement in depth measurement, which is sufficient for tracking users' visual attention in real scenes. INDEX TERMS Head-mounted gaze tracking system, saliency maps, auto-calibration, 3D gaze estimation.

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Wearable eye tracker calibration at your fingertips

Cited by 8 publications

References 13 publications

3D Gaze Estimation Using RGB-IR Cameras

3D Gaze Estimation Using RGB-IR Cameras

Calibration-Free Mobile Eye-Tracking Using Corneal Imaging

3D Gaze Estimation for Head-Mounted Eye Tracking System With Auto-Calibration Method

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