论文标题
Gazemae：使用微麦克罗自动编码器的眼睛运动的一般表示
GazeMAE: General Representations of Eye Movements using a Micro-Macro Autoencoder
论文作者
论文摘要
眼睛运动是复杂的动态事件，其中包含有关主题和刺激的大量信息。我们提出了眼球运动的抽象表示，该表示在刺激行为时保留了凝视行为的重要细微差别。我们将眼睛运动视为原始位置和速度信号，并将单独的深度时间卷积自动编码器训练。自动编码器学习与眼睛运动的快速和缓慢特征相对应的微尺度和宏观尺度表示。我们使用安装在各种分类任务上的线性分类器来评估联合表示。我们的工作准确地区分了性别和年龄段，并且优于先前关于生物识别和刺激质量化的作品。进一步的实验突出了这种方法的有效性和概括性，使眼睛跟踪研究更接近现实世界应用。
Eye movements are intricate and dynamic events that contain a wealth of information about the subject and the stimuli. We propose an abstract representation of eye movements that preserve the important nuances in gaze behavior while being stimuli-agnostic. We consider eye movements as raw position and velocity signals and train separate deep temporal convolutional autoencoders. The autoencoders learn micro-scale and macro-scale representations that correspond to the fast and slow features of eye movements. We evaluate the joint representations with a linear classifier fitted on various classification tasks. Our work accurately discriminates between gender and age groups, and outperforms previous works on biometrics and stimuli clasification. Further experiments highlight the validity and generalizability of this method, bringing eye tracking research closer to real-world applications.
