Eye Blink Detection

Here we are going to build upon this knowledge and develop a computer vision application that is capable of detecting and counting blinks in video…

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View source folder on GitHub →contributed by

@MonitSharma

Eye blink detection with OpenCV, Python, and dlib

Here we are going to build upon this knowledge and develop a computer vision application that is capable of detecting and counting blinks in video streams using facial landmarks and OpenCV.

To build our blink detector, we’ll be computing a metric called the eye aspect ratio (EAR), introduced by Soukupová and Čech in their 2016 paper

Unlike traditional image processing methods for computing blinks which typically involve some combination of:

Eye localization.

Thresholding to find the whites of the eyes.

Determining if the “white” region of the eyes disappears for a period of time (indicating a blink).

The eye aspect ratio is instead a much more elegant solution that involves a very simple calculation based on the ratio of distances between facial landmarks of the eyes.

This method for eye blink detection is fast, efficient, and easy to implement.

Understanding the “eye aspect ratio” (EAR)

In terms of blink detection, we are only interested in two sets of facial structures — the eyes.

Each eye is represented by 6 (x, y)-coordinates, starting at the left-corner of the eye (as if you were looking at the person), and then working clockwise around the remainder of the region.

Pyodide-runnable

No — it uses dlib, OpenCV, and a video/webcam stream, none of which are available in Pyodide.