Next Article in Journal
Neutron Imaging at Compact Accelerator-Driven Neutron Sources in Japan
Previous Article in Journal
High-Dynamic-Range Spectral Imaging System for Omnidirectional Scene Capture
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
J. Imaging 2018, 4(4), 54; https://doi.org/10.3390/jimaging4040054

Preventing Wine Counterfeiting by Individual Cork Stopper Recognition Using Image Processing Technologies

1,2,†,‡,* , 2,3,‡
and
1,2,‡
1
INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto 4200-465, Portugal
2
FEUP—Faculty of Engineering of the University of Porto, Porto 4200-465, Portugal
3
INESC TEC—INESC Technology and Science (formerly INESC Porto), Porto 4200-465, Portugal
Current address: Campus da FEUP, Rua Dr. Roberto Frias, 400, Porto 4200-465, Portugal
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 25 January 2018 / Revised: 2 March 2018 / Accepted: 20 March 2018 / Published: 23 March 2018
Full-Text   |   PDF [10645 KB, uploaded 26 March 2018]   |  

Abstract

Wine counterfeiting is a major problem worldwide. Within this context, an approach to the problem of discerning original wine bottles from forged ones is the use of natural features present in the product, object and/or material (using it “as is”). The proposed application uses the cork stopper as a unique fingerprint, combined with state of the art image processing techniques to achieve individual object recognition and smartphones as the authentication equipment. The anti-counterfeiting scheme is divided into two phases: an enrollment phase, where every bottle is registered in a database using a photo of its cork stopper inside the bottle; and a verification phase, where an end-user/retailer captures a photo of the cork stopper using a regular smartphone, compares the photo with the previously-stored one and retrieves it if the wine bottle was previously registered. To evaluate the performance of the proposed application, two datasets of natural/agglomerate cork stoppers were built, totaling 1000 photos. The worst case results show a 100% precision ratio, an accuracy of 99.94% and a recall of 94.00%, using different smartphones. The perfect score in precision is a promising result, proving that this system can be applied to the prevention of wine counterfeiting and consumer/retailer security when purchasing a wine bottle. View Full-Text
Keywords: wine counterfeiting; anti-counterfeiting systems; image processing; ORB; validation gate; RIOTA; object fingerprint wine counterfeiting; anti-counterfeiting systems; image processing; ORB; validation gate; RIOTA; object fingerprint
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Costa, V.; Sousa, A.; Reis, A. Preventing Wine Counterfeiting by Individual Cork Stopper Recognition Using Image Processing Technologies. J. Imaging 2018, 4, 54.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
J. Imaging EISSN 2313-433X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top