# Gaze Estimation via Strip Pooling and Multi-Criss-Cross Attention Networks
## 1 Introduction  
The goal of gaze estimation is to determine the gaze direction adopted by a person from a picture of their face or eye. With the increased development and availability of mass data and consumer technology, the demand for applications related to gaze estimation has gradually increased to include gaming, medical tools, offline retail, and distracted driving detection [1]. All these areas present the need to obtain information about the emotions, needs, behaviors, and interpersonal communication of individuals through their gazes, so as to gain insight into human cognition and behavior. The accuracy of gaze estimation has therefore come under close scrutiny as a consequence of the popularity of gaze estimation techniques. In recent years, gaze estimation methods based on deep learning [2] have established themselves as the primary methods for estimating gaze and have produced relatively good results. These approaches do not require complex hardware [3] and are robust to illumination, occlusion, and individualized differences.  
Most current gaze estimation techniques based on deep learning take their input from either face or eye images, or both. Network models that utilize both eye and face images are more complicated and therefore associated with high computational complexity, which results in slower real-time speed. Methods that only accept eye or face images as input areable to infer information more quickly and therefore better satisfy the needs of real-time applications. Furthermore, adopting face images as input produces better performance and carries greater potential for development compared with using only eye images as input [4]. However, the presence of beards, hair, eyes, hats, and other supplementary features in face images can make gaze prediction difficult. Additionally, because of potential asymmetries between the eyes, gaze estimation techniques that rely on face images must consider joint features from both eyes rather than just features from each eye separately. Most existing gaze estimation techniques do not capture these long-distance dependences between the eyes, despite their potential for substantial improvement in gaze estimation accuracy. In this paper, we propose to address this issue with a novel network called SPMCCA-Net (strip pooling and multi-criss-cross attention network), which fuses a feature-enhanced bottleneck block based on the SP (strip pooling) module [5] and the MCCA (multi-criss-cross attention) module for gaze estimation from full-face images. The main goals and achievements of our research are as follows:  
* To improve global-local feature representation and prevent redundant information unrelated to gaze from interfering with prediction, we designed a feature-enhanced bottleneck block based on the stripe pooling module to improve ResNet-50, thus enabling the backbone to capture long-distance dependencies between the eyes.
* To improve the ability of the model to locate and identify information about the area related to gaze and further improve the ability to capture long-distance dependencies between the eyes, we designed a multi-criss-cross attention module (MCCA) built upon the criss-cross attention framework [6] and incorporated this module into the backbone to capture contextual information via a self-attention mechanism.
* We also improve the based method at the loss function level by replacing the regression loss with smooth L1 loss [7]. This can greatly improve stability and convergence speed during model training and, to a large extent, also increase the accuracy of gaze estimation.