PENGENDALI POINTER DENGAN GAZE TRACKING MENGGUNAKAN METODE HAAR CLASSIFIER SEBAGAI ALAT BANTU PRESENTASI (EYE POINTER)

The application that builded in this research is a pointer controller using eye movement (eye pointer). This application is one of image processing applications, where the users just have to move their eye to control the computer pointer. This eye pointer is expected able to assist the usage of manual pointer during the presentation. Since the title of this research is using gaze tracking that follow the eye movement, so that is important to detect the center of the pupil. To track the gaze, it is necessary to detect the center of the pupil if the eye image is from the input camera. The gaze tracking is detected using the three-step hierarchy system. First, motion detection, object (eye) detection, and then pupil detection. For motion detection, the used method is identify the movement by dynamic compare the pixel ago by current pixel at t time. The eye region is detected using the Haar-Like Feature Classifier, where the sistem must be trained first to get the cascade classifier that allow the sistem to detect the object in each frame that captured by camera. The center of pupil is detect using integral projection.The final step is mapping the position of center of pupil to the screen of monitor using comparison scale between eye resolution with screen resolution. When detecting the eye gaze on the screen, the information (the distance and angle between eyes and a screen) is necessary to compute pointing coordinates on the screen. In this research, the accuracy of this application is equal to 80% at eye movement with speed 1-2 second. And the optimum mean value is between 5 and 10. The optimum distance of user and the webcam is 40 cm from webcam

Unobtrusive and pervasive video-based eye-gaze tracking

Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe

Understanding face and eye visibility in front-facing cameras of smartphones used in the wild

Commodity mobile devices are now equipped with high-resolution front-facing cameras, allowing applications in biometrics (e.g., FaceID in the iPhone X), facial expression analysis, or gaze interaction. However, it is unknown how often users hold devices in a way that allows capturing their face or eyes, and how this impacts detection accuracy. We collected 25,726 in-the-wild photos, taken from the front-facing camera of smartphones as well as associated application usage logs. We found that the full face is visible about 29% of the time, and that in most cases the face is only partially visible. Furthermore, we identified an influence of users' current activity; for example, when watching videos, the eyes but not the entire face are visible 75% of the time in our dataset. We found that a state-of-the-art face detection algorithm performs poorly against photos taken from front-facing cameras. We discuss how these findings impact mobile applications that leverage face and eye detection, and derive practical implications to address state-of-the art's limitations