We study the automation of the visual dominance ratio (VDR); a classic measure of displayed dominance in social psychology literature, which combines both gaze and speaking activity cues. The VDR is modified to estimate dominance in multi-party group discussions where natural verbal exchanges occur and other visual targets such as a table and slide screen are present. Our findings suggest that fully automated versions of these measures can estimate effectively the most dominant person in a meeting and can approximate the dominance estimation performance when manual labels of visual attention are used.