EasyGaze3D: Towards Effective and Flexible 3D Gaze Estimation from a Single RGB Camera

Eye gaze can convey rich information of human intentions, which enables the social robots to comprehend the cognition and behavior of human targets. However, the existing 3D gaze estimation methods generally have high requirements either on the dedicated hardware or the quantity and quality of training databases, which largely limits their practical application values. This paper proposes EasyGaze3D, an effective 3D gaze estimation framework using a single RGB camera. First, the framework detects the 2D facial landmarks and recovers the 3D facial shape from the input image, and derives the required camera parameters with these features. Then, without loss of generality, the gaze direction can be regarded as the vector pointing from the eyeball center to the pupil center, which are derived respectively from the detected facial landmarks and the spherical fitting performed on the recovered 3D facial shape. Besides, we propose a flexible yet efficient calibration module, namely Easy-Cali, for deriving the subject-specific 3D facial shape and eyeball centers. The features calibrated by Easy-Cali can further boost the performance of EasyGaze3D. Experimental results show that our proposed method, being plug-and-play and without the need of training on large-scale dataset, can achieve superior performance against the existing methods based on deep models.