pyBegin / projects / eye-blink-detection
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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.

    Example

    $ python main.py --shape-predictor shape_predictor_68_face_landmarks.dat \
                 --video sample_video.mp4

[INFO] loading facial landmark predictor...
[INFO] starting video stream thread...
    1. An OpenCV window opens titled “Frame” showing the video with green convex-hull outlines drawn around both eyes.
    2. The top-left corner displays the running blink count, e.g. Blinks: 3.
    3. The top-right corner shows the current EAR value, e.g. EAR: 0.31.
    4. Each time the EAR drops below 0.3 for 3 consecutive frames, the blink counter increments.
    5. Press q to stop playback and close the window.

    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.