Detection of the cent

TELKOMNIKA Low-Cost Based E C++ and OpenCV library. The the following sections.

2.3.1. Detection of the cent

The pupil may be dar contrast is sufficiently large. Y algorithm to locate the pupils constraints using a skin-color model and it will fail in the pre Yang et al applied the ellipse coordinates of pupil edge pixe This research use simple tec labeling. The steps to detect th begins by capturing a eye ima grayscaling process convert th smoothed using Gaussian filt reduce sharp edges, aiding th a d Figure 6. Detection of the cen c Gaussian blur image, d The next step is thresholding clearly visible as a black c component as well. These rep color as the pupil. The conn components in the image an component is defined as a gr component labeling algorithm Once all connected parameters of each blob such are compared to experimenta discarded based on them. Du will never be more than one Thus, the system selects the center coordinate of this conne ISSN: 1693-6930 Eye Tracking and Eye Gaze Estimation I Ketut Ge he intricacies of these subsystems will be described enter coordinate of the pupil arker than their surroundings and thresholds may . Yang et al and Stiefilhagen at al introduce an i ils by looking for two dark regions that satisfy certa lor model. Their method is limited by the results resence of other dark regions such as eyebrows a se fitting algorithm to fit a standard ellipse or cir ixels. The center of the ellipse or circle is the cente technique to detect the pupil center using conne the center of pupil is used here are shown in Figur mage frame from the camera via OpenCV’s camera t the RGB color to gray color space. After that the g filter to remove any noise in image. The smooth the pupil detection system. b c e f center of pupil, a capture frame from camera, b g d binary image, e component labelling image, f g the smoothed image to obtain the binary image. component in this image. However, there may represent areas of the image of almost exactly the nnected component labeling step exists here to and determine which is representative of the pu group of pixels with values within a certain range m used is an open source add-on for OpenCV. d component have been located, the system c uch as area, aspect ratio, roundness, and more. T tally determined values for a pupil and connected Due to the nature of the human eye and surroundin e connected component that fits all parameters f e correct connected component. The algorithm th nnected component. Gede Darma Putra 381 ed in more detail in ay be applied if the n iterative threshold rtain anthropometric lts of the skin-color s and shadows [10]. circle based on the nter of the pupil [7]. nected components gure 6. The process era subsystem. The grayscale image is thing also helps to grayscale image, , f output image e. The pupil will be ay be other black he same shade and to locate all black pupil. A connected ge. The connected calculates several . These parameters d componentes are ding features, there s for a human eye. then calculates the TELKOMNIKA Vol. 9, No. 2, 382

2.3.2. Calibration and Poin