Workflow

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We intend to follow these steps :



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Eye Detection

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The next step in the workflow is Eye detection. CODE:     ed = vision.CascadeObjectDetector('EyePairBig');     BoundBox=step(ed,J);     figure,imshow(J);     for i = 1:size(BoundBox,1)         aaa = rectangle('Position',BoundBox(i,:),'LineWidth',4,'LineStyle','-','EdgeColor','r');     end      %Crop out eye area     for i = 1:size(BoundBox,1)         J= imcrop(J,BoundBox(i,:))         figure(3),subplot(2,2,i);imshow(J);     end    
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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 same SV
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Training a Classifier

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Using an SVM Classifier to detect sleepiness: This algorithm works primarily on the principle that if the person has kept his eyes closed for too long the driver is detected to be sleepy. After extracting each frame from a video, we manually classify each image as open or closed eye. We then separate our dataset of images into training and test data - with 30 percent being training-data and the rest 60 percent being test-data. We do this split randomly. This makes our training and test data different each time we run the program. Using SVM Classifier, we generate a model based on a bag of features. We find the accuracy of the model using confusion matrices. The next step is to feed the video and to get labels as open or closed  for each new frame. Whether the eyes are open or closed in each frame. We keep track of how long the eyes have been closed continuously by resetting a count variable (that counts the elapsed time ) each time the eye opens. If the count is beyond a certain thr
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