Generalisation Approach for Banknote Authentication by Mobile Devices Trained on Incomplete Samples

Reliable Banknote Authentication is critical for economic stability. Regarding everyday use, recent studies implemented successful techniques using banknote images taken by mobile phone cameras. One challenge in mobile banknote authentication is that it is impossible to collect images by all series/brands of mobile phones. In this study, classification models are implemented that are able to generalize to the samples from a wide number of mobile phone series even though they are trained with samples from a small group of series. Existing state-of-the-art banknote authentication approaches train a separate model per sub-image of a banknote, using the extracted features of that sub-image. A new approach that trains a single global model on the concatenated features of all the sub-images is presented. Furthermore, ensemble models that combine Linear Discriminant Analysis and Deep Neural Networks are employed in order to maximize the accuracy. Implemented techniques were able to reach up to F1-score of 0.99914 on a Euro banknote data set which contain images from 16 different mobile-phone series. The results also indicate that new global model approach can improve the accuracy of the existing banknote authentication techniques in case of model training with images from restricted/incomplete phone series and brands.