Classification of Frames

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After converting the cropped eye picture to a binary image, the next step in the workflow is to classify each image as open eye or closed eye.
To do so, we take the following 3 different approaches:
1. Finding the differences between consecutive frames
2. Training a classifier
3. Finding the gradient of the image

To conclude that the driver is drowsy, we use the following approaches:
1. An average person blinks about 15 times per minute. We count the number of blinks every few seconds and compare it to this. If the frequency of blinks is unusual, we conclude that the driver is drowsy.
2. Every time there's a blink, we keep a timer for how long the drivers eye has been closed. If the time if beyond a threshold, we conclude that the driver is drowsy.

Finding the difference between consecutive frames:

We take every two consecutive frames and find the difference between the two. We can't see many white pixels in the difference image unless there is a noticeable change. A noticeable change would either mean that the person has blinked or that the person has opened his eye after a blink. 
We set a threshold for the number of white pixels we require. If the number of white pixels is greater than the threshold, we increment the number of times there's been a change. 

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