Workflow

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We intend to follow these steps :



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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 sam...
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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...
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Observations and Conclusions

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Observations    Graphical Illustration of the differences in accuracy with additions to dataset    The below figure shows the ratio of number of correctly classified images to total number of images for a test set of 30 images. This is displayed in the form of a column chart for different training data sets. On adding new coloured eye images, the accuracy of the model actually drops. This is because the classifier gets confused with more of a variety of pictures. There’s more of a conflict between images in the open set and images in the closed set. This reduces the accuracy. The algorithm to detect the lip based on colour fails as there isn’t much of a gradient colour difference. The classification of yawn using SVM classifier isn’t very accurate. With respect to the blink model, the model would perform better if the mouth area was already localised and the classifier worked on the cropped image. This wou...
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