# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 4 Experiments  
### Settings  
Dataset: In this study, we selected two datasets containing images of multiple individuals along with corresponding 3D gaze labels to validate the model, namely EyeDiap [39], MPIIFaceGaze [19] and Gaze360 [40]. These two datasets provide a wealth of visual information and accurate gaze annotations for evaluating the model's performance in handling multiple subjects and complex scenes, thereby ensuring the reliability of the experimental results and the generalization ability of the model. EyeDiap: The EyeDiap dataset is designed for gaze estimation using RGB and RGB-D cameras, featuring 94 video clips from 16 participants under varied lighting conditions and head poses, simulating real-world scenarios. A key strength of EyeDiap is its high-precision gaze annotations, including eye center positions and ping-pong ball locations,with 2D-to-3D coordinate conversion for more accurate gaze direction computation. It also provides RGB-D sensor calibration, screen calibration, camera synchronization, and head pose tracking, ensuring reliable training data. MPIIFaceGaze: MPIIFaceGaze is an extended version of the MPIIGaze dataset, containing 213,659 images from 15 participants. It enhances the original eye image dataset by incorporating full-face images and point of regard (PoG) annotations, providing richer visual information. The inclusion of full-face images helps integrate eye features, facial expressions, and head poses, which influence gaze direction, thereby improving the model's adaptability to complex scenes. For multi-stream architectures, this fusion of eye and facial features further enhances gaze estimation accuracy. Additionally, the large-scale dataset of over 210,000 images provides ample training samples for deep learning models, improving their generalization ability and ensuring stable performance across diverse environments. Gaze360: Gaze360 is a large-scale 3D gaze estimation dataset designed to support robust gaze direction estimation in natural scenes. The dataset includes 238 subjects with a wide range of gender, age, and ethnicity, captured using a Ladybug5 360\({}^{\circ}\) camera, providing precise 3D gaze annotations with an average error of 2.9\({}^{\circ}\). It contains over 2 million images, covering 360\({}^{\circ}\) head poses, with 129K training images, 17K validation images, and 26K test images. The dataset supports extreme head pose variations, partial occlusion, and dynamic video data. The diversity and real-world conditions of Gaze360 help improve the generalization ability of models, enabling them to adapt to complex environments. It is widely used in applications such as augmented reality, remote interaction, and driver monitoring. Baseline: In this experiment, to validate the rationality and effectiveness of our model design, we selected the Dilated-Net [41] gaze estimation model, proposed in previous research, as the baseline for performance comparison. Dilated-Net effectively enhances the ability to capture appearance features by introducing dilated convolutions and a multi-region network design, thereby improving gaze estimation accuracy based on appearance. The model performs especially well when handling small angular changes. Additionally, Dilated-Net shares the same input data composition as the MSMI-Net proposed in this study, using left and right eye images along with facial images as input features. Therefore, performing a performance comparison under the same input conditions ensures fairness in the experiment and allows for a more accurate assessment of the improvements made by MSMI-Net in feature fusion and information extraction. As a model that achieves good performance in existing research, Dilated-Net is highly representative. Its network structure shares similarities with MSMI-Net but also contains key design differences.