Testing Our Models

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Testing our Blink Detection models

We test the blink model (The differences method) and eye model ( Classifier) with multiple test cases. With respect to the blink model, we test our model with images of:
• Different eye colours
• Different sizes

The first method of finding differences works well with all the above cases as differences are noticed irrespective of the above conditions. The threshold values for the number of white pixels have to be changed for different types.
Colored eye bw binary image.

 In the second method of training a classifier, we initially have a test data set of only images of a particular eye colour and size.
 Results with only one type of image in dataset

This model gives us great results for different test cases of similar size and colour. The confusion matrix for this along with average accuracy for each run is displayed. Each run picks a random set of training and test data as mentioned previously. This reduces bias and overfitting. Taking the average of the runs, we get an accuracy of about 0.92. This works surprisingly well with images of different eye colours as well. 36/40 frames were classified correctly in the first run and 35/40 in the second run. When we feed in images of smaller eyes, 13 out of 30 were detected correctly in the first run and 16 out of 30 in the second run. This gives us a terrible accuracy and the model isn’t very reliable. After adding images of smaller eyes to our dataset, we get better results as displayed.Two runs are displayed in the image.
 Results with small eye type of image added dataset

With multiple runs, we get an average accuracy of 0.82. On adding images of different colours to the dataset too, we get an average accuracy of 0.80.

 Results with all eye types of image added dataset

Graphical Illustration of the differences in accuracy with additions to dataset 



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