Recognition of Banknotes in Multiple Perspectives Using Selective Feature Matching and Shape Analysis

Reliable banknote recognition is critical for detecting counterfeit banknotes in ATMs and help visual impaired people. To solve this problem, it was implemented a computer vision system that can recognize multiple banknotes in different perspective views and scales, even when they are within clutter...

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Bibliographic Details
Published in2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC) pp. 235 - 240
Main Authors Costa, Carlos M., Veiga, Germano, Sousa, Armando
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2016
Subjects
banknote recognition
computer vision
Feature extraction
feature matching
Image recognition
Image resolution
Robustness
Shape
shape analysis
Target recognition
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Summary:Reliable banknote recognition is critical for detecting counterfeit banknotes in ATMs and help visual impaired people. To solve this problem, it was implemented a computer vision system that can recognize multiple banknotes in different perspective views and scales, even when they are within cluttered environments in which the lighting conditions may vary considerably. The system is also able to recognize banknotes that are partially visible, folded, wrinkled or even worn by usage. To accomplish this task, the system relies on computer vision algorithms, such as image preprocessing, feature detection, description and matching. To improve the confidence of the banknote recognition the feature matching results are used to compute the contour of the banknotes using an homography that later on is validated using shape analysis algorithms. The system successfully recognized all Euro banknotes in 80 test images even when there were several overlapping banknotes in the same test image.
DOI:10.1109/ICARSC.2016.59