# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 1 Introduction  
Gazing is an act of looking at something, and as one of the non-verbal interaction cues, the direction of gaze reflects meaningful attention information. Gaze estimation aims to estimate the direction of the line of sight. Gaze estimation technology is widely applied in fields such as human-computer interaction [1, 2], augmented reality or virtual reality [3, 4], medical diagnosis [5, 6], and assisted driving [7]. Therefore, the importance of gaze estimation in multiple fields has made it a widely studied area in computer vision in recent years. In recent years, gaze estimation can mainly be divided into model-based methods [8, 9, 10] and appearance-based methods [11, 12, 13, 14]. Model-based methods, as traditional approaches, primarily involve geometric modeling of the eye, utilizing the geometric relationships between different facial and ocular features (facial landmarks, cornea, pupil, etc.) to calculate the three-dimensional gaze direction. They rely on external light sources to detect ocular features or observe the pupil center and iris edge to infer the direction of gaze. Model-based methods can achieve high precision and tolerate head movements, thus being used in commercial eye trackers. However, they require personal calibration to achievegood accuracy, making the use of eye-tracking systems less user-friendly and reducing user experience [15]. Appearance-based gaze estimation methods are a technique that directly extracts features from ocular images and predicts gaze direction. These methods do not require complex 3D eye models or expensive specialized equipment but rely on extracting features from 2D images for gaze direction prediction. Early studies typically employed handcrafted features, such as Local Binary Patterns (LBP) or Histogram of Oriented Gradients (HOG), to describe ocular image characteristics [16]. However, these methods often encounter performance bottlenecks when handling complex scenarios, such as low resolution, occlusion, or variations in lighting conditions [17; 18]. With the rapid advancement of deep learning technology, appearance-based gaze estimation methods have leveraged the powerful feature extraction capabilities of convolutional neural networks (CNN) to learn the complex mapping relationships between gaze direction and visual features from large-scale ocular image datasets. [PERSON] et al. [19] first proposed a CNN-based gaze estimation method, significantly improving the accuracy of appearance-based gaze estimation. Building on this, [PERSON] et al. [20] introduced an asymmetric regression network (AR-Net) by modeling the asymmetry between the two eyes, further optimizing estimation performance. To address the limitations of CNN in global feature modeling, [PERSON] et al. [21] integrated transformers with CNN to extract richer gaze features from facial images, achieving superior estimation results. In recent years, [PERSON] et al. [22] leveraged the asymmetry in appearance and feature space by extracting low-level semantic features through the main branch while utilizing agent tasks to capture high-level asymmetric information. This approach optimized eye feature learning, significantly improving the accuracy and generalization ability of gaze estimation. The FSIGaze model [23] has further advanced this direction by incorporating both spatial and frequency domain information and introducing residual modules and the Frequency-Spatial Synergistic (FSS) module, effectively enhancing gaze estimation accuracy. Despite the significant progress of appearance-based gaze estimation algorithms in diverse scenarios, most existing methods rely on a single modality input (such as eye or facial images), failing to fully exploit the intrinsic correlation between the two. Additionally, while some eye-face fusion models consider multimodal feature integration, they often overlook the synergistic effect between independent ocular features and fused features, limiting further improvements in estimation performance.