We have started a discussion of image processing techniques using Orange in a few blog posts previously. Let us look at another case that can be utilized in the Data Mining Tool Orange. In this time, we will look at more current problem, that is identifying the Mask & Non-Faces Using.
As you know every prediction problem needs two solutions. First, it needs to build the model using the prediction techniques and then it needs to choose the higher accurate model and build the production application.
Since this is a classification problem, we need a data set that is already classified. Following is the already classified images.
PN: Today being the January 20th and the images of US president and Vice presidents images are also in the Non-Mask category. It is not a deliberate just a coincident.
Let us build models from different classification techniques and find out what is the best technique.
In above, we have used five classification techniques, such as Naive Bayes, Random Forest, SVM, Neural Network, and Logistic Regression. Image Embedding is the special control available in Orange in order to perform the image analysis. We have included a Test and Score Control in order to verify the accuracy and other model parameters such as Precision, Recall and F1 measure etc.
The Above results show that both Neural Network and Logistic Regression has 100% accuracy over the other techniques.
Now let us move to the next step, which is the prediction part.
Let us select some challenging images rather than selecting naive images for the prediction.
The first image, yes the mouth is closed but with hands. The second image is very straight forward. Next two images are with a mask but with a transparent mask.
Let us look at the predictions.
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