Facial Feature Tracking and Head Pose Tracking as Input for Platform Games

Sammanfattning: Modern facial feature tracking techniques can automatically extract and accurately track multiple facial landmark points from faces in video streams in real time. Facial landmark points are defined as points distributed on a face in regards to certain facial features, such as eye corners and face contour. This opens up for using facial feature movements as a handsfree human-computer interaction technique. These alternatives to traditional input devices can give a more interesting gaming experience. They also open up for more intuitive controls and can possibly give greater access to computers and video game consoles for certain disabled users with difficulties using their arms and/or fingers. This research explores using facial feature tracking to control a character's movements in a platform game. The aim is to interpret facial feature tracker data and convert facial feature movements to game input controls. The facial feature input is compared with other handsfree inputmethods, as well as traditional keyboard input. The other handsfree input methods that are explored are head pose estimation and a hybrid between the facial feature and head pose estimation input. Head pose estimation is a method where the application is extracting the angles in which the user's head is tilted. The hybrid input method utilises both head pose estimation and facial feature tracking. The input methods are evaluated by user performance and subjective ratings from voluntary participants playing a platform game using the input methods. Performance is measured by the time, the amount of jumps and the amount of turns it takes for a user to complete a platform level. Jumping is an essential part of platform games. To reach the goal, the player has to jump between platforms. An inefficient input method might make this a difficult task. Turning is the action of changing the direction of the player character from facing left to facing right or vice versa. This measurement is intended to pick up difficulties in controling the character's movements. If the player makes many turns, it is an indication that it is difficult to use the input method to control the character movements efficiently. The results suggest that keyboard input is the most effective input method, while it is also the least entertaining of the input methods. There is no significant difference in performance between facial feature input and head pose input. The hybrid input version has the best results overall of the alternative input methods. The hybrid input method got significantly better performance results than the head pose input and facial feature input methods, while it got results that were of no statistically significant difference from the keyboard input method. Keywords: Computer Vision, Facial Feature Tracking, Head Pose Tracking, Game Control