This week, I worked on implementing off-center detection and initial set-up phase for the eye detection portion of the facial detection part. When a user’s eyes are wandering around (subpar eye contact) for up to 5 seconds, the system will alert the user that their eyes are not centered. Centered in this case means within a certain range of the coordinates detected during the initial setup phase. The center coordinates of the irises/pupils are found through using moments in OpenCV, which find the centroid of the iris/pupil image (https://docs.opencv.org/2.4/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html). The center is with respect to a specific origin, which is the left edge of each iris (in other words, 0 is the left edge of each iris, not the left edge of the screen). Each eye actually has the same center because of this origin reference.
The center coordinates are calculated for each eye detection and they are stored into an array. After the 10 seconds of initial set-up (changed from 5 from the proposal presentation, because 5 seconds was too short), the average of all the center coordinates is taken to calculate the reference center coordinates (X and Y). This reference center is what the program refers to to calculate whether or not the user’s eyes are “off-center.” I also started doing some formal testing, where I keep track of whether or not a user is alerted within 5 seconds if their eyes are wandering around. If they are, then this constitutes a passing test. If they are not, then this constitutes a failing test (false negative). If the user is alerted, but their eyes were not wandering around, this is also a failing test (false positive).
I believe that I am on-schedule, as getting the off-center detection and initial set-up phase for the eye detection is a big part of the facial detention portion. Next week, I plan on continuing to test the eye detection part, particularly on other user’s eyes (I will ask my friends if they want to volunteer). I also want to start the screen alignment portion, and research more about facial landmark detection in OpenCV.