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Using the optical mouse sensor as a two-euro counterfeit coin detector.

Marcel Tresanchez¹, Tomàs Pallejà, Mercè Teixidó

¹Department of Computer Science and Industrial Engineering, University of Lleida, Jaume II, 69, 25001 Lleida, Spain; E-Mails: mtresanchez@diei.udl.cat (M.T.); tpalleja@diei.udl.cat (T.P.); mteixido@diei.udl.cat (M.T.).

Sensors (Basel, Switzerland)

|March 9, 2012

Summary

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An optical mouse sensor can detect counterfeit two-Euro coins by comparing partial coin images. This vision-based method rivals trained users and surpasses untrained users in accuracy.

Area of Science:

Optoelectronics
Numismatics
Computer Vision

Background:

Counterfeit currency poses a significant economic challenge.
Existing counterfeit detection methods often require specialized equipment or training.
The need for accessible and low-cost detection solutions is growing.

Purpose of the Study:

To evaluate the efficacy of an optical mouse sensor as a counterfeit coin detector.
To assess the performance of this vision-based detection method for two-Euro coins.
To compare the sensor's detection accuracy against human users.

Main Methods:

Utilizing the image acquisition capabilities of an optical mouse sensor for close-range coin imaging.
Comparing partial images of analyzed coins against a reference dataset of genuine coins.

Keywords:

optical mouse sensor optical sensor vision based counterfeit

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Quantifying counterfeit acceptance and rejection rates based on image matching.

Main Results:

The optical mouse sensor achieved high accuracy in distinguishing counterfeit two-Euro coins.
Detection performance using the sensor was comparable to that of a trained human inspector.
The sensor-based method significantly outperformed untrained human inspection.

Conclusions:

An optical mouse sensor sensor presents a viable, low-cost tool for counterfeit coin detection.
Vision-based analysis using readily available technology can effectively identify currency fraud.
This approach offers a practical alternative to traditional counterfeit detection methods.