Gaze estimation holds significant importance in various applications. Pioneering research has demonstrated state-of-the-art performance in gaze estimation models by utilizing deep Convolutional Neural Networks (CNNs) and incorporating full facial images as input, instead of or in addition to solely using one or both eye images. Facial images encode crucial cues that can enhance the accuracy of gaze regression models. However, it remains unclear which specific facial features contribute and to what extent they contribute to the overall estimation accuracy. In this research, we aim to shed light on identifying the influential facial regions and quantifying their contributions to gaze estimation accuracy.