CMFS-Net: Common Mode Features Suppression Network for Gaze Estimation
Xu Xu, Lei Yang, Yan Yan, Congsheng Li · 2023
Gaze estimation typically involves determining the direction or point of gaze based on a single image of the eye or face. However, due to variations in the internal structure and morphology of eyes among individuals, existing models for gaze estimation have limited accuracy. They often exhibit subject-dependent bias and a high degree of variance in their output. To address these limitations, calibration is commonly used to improve accuracy by mapping individuals' gaze predictions to real gaze. In this study, we propose an innovative approach for gaze estimation called the image common-mode feature suppression network. By training this network to suppress common-mode features, we can forecast the difference in gaze between two input images from the same subject. Furthermore, by leveraging the inferred differences and a set of calibration images for a specific object, we can forecast the gaze direction for a new eye sample. Our hypothesis is that by contrasting the two eye images, we can significantly reduce confounding factors that typically affect single-image forecasting methods, thereby producing superior predictions. To evaluate our approach, we curated a dataset specifically for training and testing the network. The experimental results demonstrate that our proposed model achieves gaze estimation with an error of less than 5.3 mm.