Human perception based counterfeit detection for automated teller machines
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
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Automated teller machines obviously require functions for protecting themselves from crimes, because they must handle cash. This paper discusses self-defense-technologies based on image processing and recognition to realize such functions in the machines. The technologies include (i) banknote validation for preventing machines from receiving counterfeit banknotes, (ii) form & character recognition for preventing machines from accepting remittance forms with out of due dates, (iii) person identification to stop machines from transacting with non-customers, and (iv) object recognition to guard machines against foreign objects such as spy cams that may be attached to them. After describing the outline of the system, technologies (i), (ii), and (iii) are introduced. This paper concentrates on the object recognition technology for detecting foreign objects. Although the technology is based on conventional background-subtraction, we developed special noise reduction techniques to make it suitable for actual machines. The object recognition technology was evaluated in experiments using real 160-hour image video composed of about 8.6 x 106 frames in total, and the false-alarm rate and false-negative rate were 0.7% and 0%, respectively.