Mobgazenet: robust gaze estimation mobile network based on progressive attention mechanisms

Abstract Gaze estimation is a fundamental task in computer vision with a wide range of applications. Recently, convolution neural network approaches have made notable progress in inferring gaze from facial images. However, these methods often struggle to capture fine-grained gaze features and reflect spatial contextual relationships, as the most crucial gaze information exists in the eye area, which constitutes only a small portion of the face images. In this paper, we introduce MobGazeNet, an efficient and lightweight network that leverages a progressive combination of attention mechanisms, including squeeze-and-excitation, convolutional block attention module, and coordinate attention. The combination of attention mechanisms helps to emphasize crucial eye features and allows the model to consider both local and global spatial relationships without increasing computational overhead. Furthermore, we introduce the rotation matrix formalism for gaze ground truth to avoid discontinuity and ambiguity in spherical angle representation. Building upon this, we propose a continuous 6D rotation matrix representation to enable efficient and reliable direct regression which we further enhance with a geodesic-based loss. To evaluate our model, we conduct experiments on three popular datasets collected in unconstrained settings. Our proposed model surpasses current SOTA methods in both performance and efficiency, showcasing its superior capability in gaze estimation. Our code is available at: https://github.com/Ahmednull/MobGazeNet .