The human face is central to our everyday social interactions. Recent studies have shown that while gazing at faces, each one of us has a particular eye-scanning pattern, highly stable across time. Although variables such as culture or personality have been shown to modulate gaze behavior, we still don't know what shapes these idiosyncrasies. Moreover, most previous observations rely on static analyses of small-sized eye-position data sets averaged across time. Here, we probe the temporal dynamics of gaze to explore what information can be extracted about the observers and what is being observed. Controlling for any stimuli effect, we demonstrate that among many individual characteristics, the gender of both the participant (gazer) and the person being observed (actor) are the factors that most influence gaze patterns during face exploration. We record and exploit the largest set of eye-tracking data (405 participants, 58 nationalities) from participants watching videos of another person. Using novel data-mining techniques, we show that female gazers follow a much more exploratory scanning strategy than males. Moreover, female gazers watching female actresses look more at the eye on the left side. These results have strong implications in every field using gaze-based models from computer vision to clinical psychology.