Abstract
This study evaluates a smartphone-based, deep-learning eye-tracking algorithm by comparing its performance against a commercial infrared-based eye tracker, the Tobii Pro Nano. The aim is to investigate the feasibility of appearance-based gaze estimation under realistic mobile usage conditions. Key sensitivity factors, including age, gender, vision correction, lighting conditions, device type, and head position, were systematically analysed. The appearance-based algorithm integrates a lightweight convolutional neural network (MobileNet-V3) with a recurrent structure (Long Short-Term Memory) to predict gaze coordinates from grayscale facial images. Gaze data were collected from 51 participants using dynamic visual stimuli, and accuracy was measured using Euclidean distance. The deep learning model produced a mean error of 17.76 mm, compared to 16.53 mm for the Tobii Pro Nano. While overall accuracy differences were small, the deep learning-based method was more sensitive to factors such as lighting, vision correction, and age, with higher failure rates observed under low-light conditions among participants using glasses and in older age groups. Device-specific and positional factors also influenced tracking performance. These results highlight the potential of appearance-based approaches for mobile eye tracking and offer a reference framework for evaluating gaze estimation systems across varied usage conditions.
Abstract (translated)
这项研究评估了一种基于智能手机的深度学习眼动追踪算法，通过将其性能与商用红外眼动追踪器Tobii Pro Nano进行比较来实现。目的是在现实中的移动使用条件下调查外观基础的眼球注视点估计方法的可行性。系统地分析了包括年龄、性别、视力矫正、光照条件、设备类型和头部位置等关键敏感因素。 基于外观的方法结合了一种轻量级卷积神经网络（MobileNet-V3）与递归结构（长短期记忆，LSTM），以从灰度面部图像中预测注视点坐标。使用动态视觉刺激收集了51名参与者的眼动数据，并用欧氏距离来测量准确性。 深度学习模型产生的平均误差为17.76毫米，相比之下Tobii Pro Nano的误差为16.53毫米。尽管总体准确性的差异较小，但基于深度学习的方法对于诸如光照、视力矫正和年龄等因素更敏感，在使用眼镜的参与者和老年群体中，在低光条件下观察到了较高的失败率。 特定设备和位置因素也影响了追踪性能。这些结果突显了在移动眼动追踪中外观基础方法的应用潜力，并为评估不同使用条件下的注视点估计系统提供了一个参考框架。
URL
https://arxiv.org/abs/2506.11932
