Application of a Strong Tracking Finite-Difference Extended Kalman Filter to Eye Tracking

Since eye tracking was first introduced by Mowrer in 1936, it has been gaining in popularity over the past decades as a window into observers’ visual and cognitive process. For instance, researchers have utilized eye tracking to study behavior in such domains as driver fatigue detection (Qiang et al., 2004; Horng et al., 2004; Dong et al., 2004 ), eye typing for helping users with movement disabilities interact with computers (Majaranta & Raiha, 2002), eye tracking analysis of user behavior in WWW search (Laura et al., 2004), using eye tracking techniques to study collaboration on physical tasks for medical research, VR system for measuring inspection methods, and image scanning (Noton & Stark, 1971). Above all applications, two types of human-computer interfaces utilize eye tracking, passive and active interfaces. Passive interfaces monitor the user’s eye movements and automatically adapt themselves to the user. For example in driver fatigue detection, the researchers track the driver eyes to fatigue detection, because the human eyes express the most direct reaction when dozing, inattention and yawning. On the other hand, Active interfaces allow users to explicitly control the interface though the use of eye movements. For example, eye typing has users look at keys on a virtual keyboard to type instead of manually depressing keys as on a traditional keyboard (Majaranta & Raiha, 2002; Takehiko et al., 2003). Such active interfaces have been quite effective at helping users with movement disabilities interact with computers. Not surprisingly, eye tracking has attracted the interest of many researchers, and eye trackers have been commercially available for many years (Qiang et al., 2004; Horng et al., 2004; Takehiko et al., 2003; John et al., 2005). In the past decades, many researchers have paid attention to the eye tracking in human computer interaction. There have been many methods that support non-invasive eye tracking. In (Li et al., 2005), all of these eye tracking algorithms can be classified into two approaches: feature-based and model-based approaches. Feature-based approaches detect and localize image features related to the position of the eye. A commonality among featurebased approaches is that a criteria (e.g., a threshold) is needed to decide when a feature is present or absent. The determination of an appropriate threshold is typically left as a free parameter that is adjusted by the user. The tracked features vary widely across algorithms but most often rely on intensity levels or intensity gradients. For example, in infrared (IR) images created with the dark-pupil technique, an appropriately set intensity threshold can