This research report explores the role of eye gaze in human-robot interactions and proposes a learning system for detecting objects gazed at by humans using solely visual feedback. The system leverages face detection, human attention prediction, and online object detection, and it allows the robot to perceive and interpret human gaze accurately, paving the way for establishing joint attention with human partners. Additionally, a novel dataset collected with the humanoid robot iCub is introduced, comprising over 22,000 images from ten participants gazing at different annotated objects. This dataset serves as a benchmark for the field of human gaze estimation in table-top human-robot interaction (HRI) contexts. In this work, we use it to evaluate the performance of the proposed pipeline and examine the performance of each component. Furthermore, the developed system is deployed on the iCub, and a supplementary video showcases its functionality. The results demonstrate the potential of the proposed approach as a first step to enhance social awareness and responsiveness in social robotics, as well as improve assistance and support in collaborative scenarios, promoting efficient human-robot collaboration.