Facial analysis is a promising approach to detect emotions of players unobtrusively; however approaches are commonly evaluated in contexts not related to games or facial cues are derived from models not designed for analysis of emotions during interactions with games. We present a method for automated analysis of facial cues from videos as a potential tool for detecting stress and boredom of players behaving naturally while playing games. Computer vision is used to automatically and unobtrusively extract 7 facial features aimed at detecting the activity of a set of facial muscles. Features are mainly based on the Euclidean distance of facial landmarks and do not rely on predefined facial expressions, training of a model, or the use of facial standards. An empirical evaluation was conducted on video recordings of an experiment involving games as emotion elicitation sources. Results show statistically significant differences in the values of facial features during boring and stressful periods of gameplay for 5 of the 7 features. We believe our approach is more user-tailored, convenient, and better suited for contexts involving games.