Authenticating Banknotes Based on Images
Simple and accurate solution with hyperplane classification trees
Automating the authentication procedure for banknotes
With image data on genuine and forged banknotes, we aimed to develop an automated procedure to differentiate the forged ones as part of the authentication process.
The features from the images were extracted using wavelet transform tools, yielding information on the variance, skewness, curtosis, and entropy of the image. We used Optimal Classification Trees to learn which feature combinations help distinguish between genuine and forged banknotes. The learned decision tree achieved an Area-Under-the-Curve (AUC) of 99.3%, indicating near-perfect performance.
Optimal Classification Tree predicting the banknote authenticity
Optimal Classification Trees offer unparalleled flexibility with hyperplanes
In the past, decision trees with hyperplane splits were impractical to fit on even moderately-sized datasets. However, it is straightforward to extend the global optimization model that powers Optimal Trees to incorporate hyperplane splits. This means that Optimal Trees are unique in offering a practical way to train these hyperplane split trees at scale, allowing us to revisit the problem with more modeling power.
Example hyperplane splits into three classes using two variables
Shallower and simpler trees with hyperplanes
This simple solution not only automates the banknote authentication process with near-perfect accuracy, but also permits much easier verification of the decision process by experts as a result of the reduced size of the tree.
Optimal Classification Tree with hyperplanes predicting the banknote authenticity
Why is the Interpretable AI solution unique?
More modeling flexibility
The ability to incorporate hyperplanes at scale unlocks unprecedented modeling power for decision trees.
Simpler models for inspection
Introducing hyperplane splits into the tree reduced the size significantly, making it easier to inspect and verify the model.