EasyGaze：适用于手持移动设备的混合眼动追踪方法

Q1 Computer Science Virtual Reality Intelligent Hardware Pub Date : 2022-04-01 DOI:10.1016/j.vrih.2021.10.003

Shiwei Cheng, Qiufeng Ping, Jialing Wang, Yijian Chen

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引用次数: 7

摘要

背景用于移动设备的眼动追踪技术已经取得了重大进展。然而，由于计算能力有限和上下文的复杂性，传统的基于图像特征的技术无法准确提取特征，从而影响了性能。方法本研究将基于外观和特征的眼动追踪方法相结合，提出了一种新的方法。进行面部和眼睛区域检测以获得用作外观模型的输入的特征，以检测特征点。利用特征点生成角中心瞳孔中心等特征向量，计算注视坐标。结果为了获得性能最好的特征向量，我们比较了不同图像分辨率和光照条件下的不同向量，结果表明，当图像分辨率为96×48像素时，光源从眼睛前部照射，在1.93°的视角下，平均凝视精度达到。结论与现有方法相比，该方法提高了注视注视的准确性，具有较好的实用性。

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EasyGaze: Hybrid eye tracking approach for handheld mobile devices

Background

Eye-tracking technology for mobile devices has made significant progress. However, owing to limited computing capacity and the complexity of context, the conventional image feature-based technology cannot extract features accurately, thus affecting the performance.

Methods

This study proposes a novel approach by combining appearance- and feature-based eye-tracking methods. Face and eye region detections were conducted to obtain features that were used as inputs to the appearance model to detect the feature points. The feature points were used to generate feature vectors, such as corner center-pupil center, by which the gaze fixation coordinates were calculated.

Results

To obtain feature vectors with the best performance, we compared different vectors under different image resolution and illumination conditions, and the results indicated that the average gaze fixation accuracy was achieved at a visual angle of 1.93° when the image resolution was 96 × 48 pixels, with light sources illuminating from the front of the eye.