Accurate, robust, inexpensive gaze tracking in the car can help keep a driver safe by facilitating the more effective study of how to improve (1) vehicle interfaces and (2) the design of future Advanced Driver Assistance Systems. In this paper, we estimate head pose and eye pose from monocular video using methods developed extensively in prior work and ask two new interesting questions. First, how much better can we classify driver gaze using head and eye pose versus just using head pose? Second, are there individual-specific gaze strategies that strongly correlate with how much gaze classification improves with the addition of eye pose information? We answer these questions by evaluating data drawn from an on-road study of 40 drivers. The main insight of the paper is conveyed through the analogy of an "owl" and "lizard" which describes the degree to which the eyes and the head move when shifting gaze. When the head moves a lot ("owl"), not much classification improvement is attained by estimating eye pose on top of head pose. On the other hand, when the head stays still and only the eyes move ("lizard"), classification accuracy increases significantly from adding in eye pose. We characterize how that accuracy varies between people, gaze strategies, and gaze regions.
Pupil detection approaches have been extensively studied. Methods usually track corneal reflection, distinct pupil shape in combination with edge-detection, characteristic light intensity of the pupil, or a 3D model of the eye to derive an estimate of an individual's pupil, iris, or eye position @cite_8 . Our approach uses an adaptive CDF-based method @cite_14 in conjunction with face alignment that significantly narrows the search space.
@cite_14 @cite_8
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This paper presents a novel adaptive algorithm to detect the center of pupil in frontal view faces. This algorithm, at first, employs the viola-Jones face detector to find the approximate location of face in an image. The knowledge of the face structure is exploited to detect the eye region. The histogram of the detected region is calculated and its CDF is employed to extract the eyelids and iris region in an adaptive way. The center of this region is considered as the pupil center. The experimental results show ninety one percent's accuracy in detecting pupil center. Eye-gaze detection and tracking have been an active research field in the past years as it adds convenience to a variety of applications. It is considered a significant untraditional method of human computer interaction. Head movement detection has also received researchers' attention and interest as it has been found to be a simple and effective interaction method. Both technologies are considered the easiest alternative interface methods. They serve a wide range of severely disabled people who are left with minimal motor abilities. For both eye tracking and head movement detection, several different approaches have been proposed and used to implement different algorithms for these technologies. Despite the amount of research done on both technologies, researchers are still trying to find robust methods to use effectively in various applications. This paper presents a state-of-art survey for eye tracking and head movement detection methods proposed in the literature. Examples of different fields of applications for both technologies, such as human-computer interaction, driving assistance systems, and assistive technologies are also investigated.