This paper presents analysis of a previously recorded multimodal interaction dataset. The primary purpose of that dataset is to explore patterns in the focus of visual attention of humans under three different conditions - two humans involved in task-based interaction with a robot; the same two humans involved in task-based interaction where the robot is replaced by a third human, and a free three-party human interaction. The paper presents a data-driven methodology for automatic visual identification of the active speaker based on facial action units (AUs). The paper also presents an evaluation of the proposed methodology on 12 different interactions with an approximate length of 4 hours. The methodology will be implemented on a robot and used to generate natural focus of visual attention behavior during multi-party human-robot interactions