# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 2 Related Work
### Appearance-Based Gaze Estimation  
Appearance-based gaze estimation methods primarily rely on extracting information from the appearance features of the eyes or face to estimate an individual's gaze direction. These methods typically use a single RGB camera to capture images and apply deep learning techniques to learn the mapping relationship between image data and gaze direction. Early work by [PERSON] et al. [29] proposed a gaze tracking system based on artificial neural networks, which uses a head-mounted device to capture individual eye images and employs neural networks to map gaze functions. To enhance the generalization capability of appearance-based methods, [PERSON] et al. [30] introduced a gaze estimation method based on visual saliency maps, which does not require explicit personal calibration. By using Gaussian process regression to establish a mapping between eye images and gaze points, and employing a feedback loop from the gaze estimator to the gaze probability map, this approach improves gaze estimation accuracy, making appearance-based methods applicable to different populations. However, early appearance-based gaze estimation techniques faced a series of challenges, primarily due to their high dependence on the sampling environment, posture, and individual differences. Due to these limitations, early appearance-based gaze estimation techniques struggled to adapt to different users, environments, and postures, making it difficult to achieve stable and accurate gaze tracking results in diverse application scenarios. With the continuous advancement of deep learning technologies, CNNs have been widely applied in the field of computer vision. In 2016, [PERSON] et al. [31] collected and released a large-scale eye-tracking dataset named GazeCapture and developed an eye-tracking model based on CNN called iTracker. This model performed exceptionally well in comprehensive evaluations on the GazeCapture dataset, significantly outperforming existing methods at the time. Subsequently, [PERSON] et al. [32] proposed another important eye-tracking dataset, MPIGaze, which was collected in real-world environments. They constructed a GazeNet model using a 16-layer VGG deep convolutional neural network to learn the mapping from head pose and eye images to 3D gaze direction. To overcome the limitations of using only eye or face images as input for gaze estimation, [PERSON] et al. [33] proposed the CA-Net model, which adopts a coarse-to-fine strategy to improve gaze direction estimation accuracy. CA-Net extracts coarse-grained features from facial images using Face-Net to estimate the basic gaze direction, while Eye-Net estimates gaze residuals from left and right eye images to refine the base gaze direction. However, this method processes eye and face images separately during feature extraction, without fully considering the potential feature correlations between them, thus limiting the model's performance. To address this issue, [PERSON] et al. [34] introduced a feature fusion method based on multi-level information element integration. This method extracts multi-level features from the raw images and constructs a multi-level information element matrix while incorporating the motion transmission principles of human eye gaze behavior to design a combination pattern for information elements. This approach improves the interpretability of appearance-based gaze estimation models while enhancing their practicality. However, although this method strengthens the correlation between features, it gives limited consideration to the specific impact of eye features and fused features on gaze estimation results, leading to certain limitations in specific cases. Additionally, 3 DGazeNet [35] detects five key points on the face, crops the full face image along with the left and right eye images, and stacks these three images along the channel dimension as input. Using a parallel network structure and a special loss function, it completes the gaze estimation task.