Vaheeshta’s Status Report for 2/20

This week for me involved working on our project proposal and researching eye detection and classification methods. My main focus on our project proposal was researching and defining our testing, verification, and metrics for our nine requirements. I also dove into learning the basics of facial landmarking and eye detection as well as researching various eye detection methods. Some eye detection algorithms I identified in various research papers and online literature are as follows: Peng Wang, Matthew B. Green, and Qiang Ji’s Haar wavelet and AdaBoost algorithm that has a 94.5% eye detection accuracy; the Viola-Jones algorithm from the OpenCV library; D. Sidibe, P. Montesinos, S. Janaqi’s algorithm with a 98.4% detection rate; and Dlib’s Convolutional Neural Networks. Since computer vision is a very new topic for me, I taught myself an introduction to Haar cascade classifiers using OpenCV, some basics of Histogram of Oriented Gradients (HOG) using Dlib, and Convolutional Neural Networks (CNN) using Dlib. For instance, Dlib’s facial landmarking detector produces 68 (x, y) coordinates that map to specific facial structures, such as the left eye with [42, 48] and the right eye with [36, 42].

The next step involves finding which algorithm would be the best option for our project, taking both accuracy and performance into consideration. At this rate, my progress is right on schedule.