Yawn Detection

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Yawn Detection using image processing


We add a yawn detector to our system to provide a more complete evaluation process to detect sleepiness.
Before we detect yawns, we need to detect the mouth region.
To detect the mouth, we use 2 approaches:

1. Use the viola Jones algorithm as we did for the face and eye. This method can be used to detect the mouth but fails to detect yawns.

2.  Find the mouth area based on the colour of the lip. Segment out the red area as the mouth. extract the red plane and subtract it with the gray scale/blue plane/green plane image to get high-intensity values only around the lip region. This can then be labeled as the lip. Figure 6 shows how this works. The red Channel is subtracted with the green Channel in this case to localize the lip. This method doesn’t work unless there is such a drastic change.

The other method we’ve used to detect yawns is to train a classifier: This is similar to training a classifier to classify blink. We use the same SVM classifier with a different dataset. Once the yawn is detected, we find the frequency of the yawn. If it crosses a threshold, then we set off the alarm.

The actual image with intensified red color and the difference Image.

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