Efficient 2D/3D Gaze Estimation Using TGGNet: A Transformer Graph Approach

The human eye gaze is a crucial visual and cognitive attention indicator, with broad applications in intelligent vehicle systems and human-machine interaction. This paper presents a novel gaze estimation approach using Graph Neural Networks (GNNs), leveraging the geometric relationship between facial landmarks and gaze direction. Existing appearance-based gaze estimation approaches primarily rely on raw facial images, often overlooking the spatial relationships between facial landmarks and gaze direction. Additionally, many recent methods involve large, computationally expensive models, limiting their applicability in real-time scenarios. Facial landmarks serve as graph nodes, and spatial distances form the edges. We demonstrate significant correlations between node positions and gaze direction, as well as between edge lengths and head pose. Our Transformer Graph Gaze Network (TGGNet) processes this graph-based data to estimate the gaze direction. The lightweight Transformer-based GNN model, with approximately 3.72 million parameters and only 0.76 Giga FLOPs, is highly suitable for real-time systems, offering both computational efficiency and low memory requirements. TGGNet assigns higher attention weights to key landmarks, improving gaze estimation. We validated the model on GazeCapture and MPIIFaceGaze (2D) and Gaze360 (3D), showing superior performance. Attention map analysis highlights the importance of landmarks around the eyes, particularly the pupils, irises, and eyelids. Video demos and codes can be found on our project’s repository https://github.com/AiX-Lab-UWO/GazeTGGNet.