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Low-cost Geometry-based Eye Gaze Detection using Facial Landmarks Generated through Deep Learning

2023-12-31 05:45:22
Esther Enhui Ye, John Enzhou Ye, Joseph Ye, Jacob Ye, Runzhou Ye


Introduction: In the realm of human-computer interaction and behavioral research, accurate real-time gaze estimation is critical. Traditional methods often rely on expensive equipment or large datasets, which are impractical in many scenarios. This paper introduces a novel, geometry-based approach to address these challenges, utilizing consumer-grade hardware for broader applicability. Methods: We leverage novel face landmark detection neural networks capable of fast inference on consumer-grade chips to generate accurate and stable 3D landmarks of the face and iris. From these, we derive a small set of geometry-based descriptors, forming an 8-dimensional manifold representing the eye and head movements. These descriptors are then used to formulate linear equations for predicting eye-gaze direction. Results: Our approach demonstrates the ability to predict gaze with an angular error of less than 1.9 degrees, rivaling state-of-the-art systems while operating in real-time and requiring negligible computational resources. Conclusion: The developed method marks a significant step forward in gaze estimation technology, offering a highly accurate, efficient, and accessible alternative to traditional systems. It opens up new possibilities for real-time applications in diverse fields, from gaming to psychological research.

Abstract (translated)

简介:在人类-计算机交互和行为研究的领域,精确实时眼神检测是至关重要的。传统方法通常依赖于昂贵的设备或大量数据,这在许多场景下是不切实际的。本文介绍了一种新颖的基于几何的方法来解决这些挑战,利用消费级硬件实现更广泛的适用性。方法:我们利用具有快速检测消费者级芯片上面部关键点的神经网络来生成准确且稳定的面部和眼睛的三维关键点。从中,我们导出一个基于几何的描述符,构成一个8维的流形,表示眼和头的运动。这些描述符随后被用来形成预测眼 gaze 方向的线性方程。结果:我们的方法在角误差不到1.9度的情况下,展示了与最先进的系统相媲美的能力,同时在实时操作中,且对计算资源的需求非常小。结论:所开发的方法在目光检测技术上取得了显著的突破,为传统系统提供了一种高准确度、高效和易用性的替代方案。这为各种领域的实时应用提供了新的可能性,从游戏到心理学研究。



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