Abstract:
Science takes part to observe eyes behavior, and it
and make things easier to do. The previous research shows that when a
something the pupil dilate and when a human dislike something the pupil
Generally, it is quite difficult to know someone’s feelings about likeness and
ensure
through eyes
human like
contracts.
dis likeness towards specific activities. In this study pupil detections algorithm is
divided into three main parts, capturing of eyes, segmentation of pupil and algorithm
which fits circles around the iris and pupil to measure pupil/iris ratio. The objective of
this project is to develop the system that detects which activity human
on computer through tracking their blinking patterns, because blinking rates varies for
different activities and likeness and dis likeness towards those activities by tracking
user’s pupil size. This project is divided in two subsequent parts identifying blinking
had first trace the face of
is performing
patterns and tracking pupil size. For blinking we rates or
human using shape_predictor_68_face_landmarks.dat datasets and captures video
from live video stream using cv2.VideoCapture() library’s object then we got the both
left and right eyes landmarks, draw contours on the eyes and applied some more
mathematical calculation to detect blinking of eyes. Finally,
modules give 71.54% accuracy when was tested
we had concluded by
evaluating the results that
properly, which is better than previous related researches
