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technologies to combat counterfeiting (Bernardi,
2008). Chromatography and DNA analysis
techniques have been conducted to inspect agri-
products themselves to prevent counterfeiting (Lees,
2003). Numerous anti-counterfeit technologies have
been utilized, and surveillance has been conducted
by public institutions. However, counterfeiting of
various agri-products is increasingly being reported.
1.2 Problems in Traceability of
Agri-Product
Existing methods encounter two main difficulties in
being effective deterrents against counterfeiting:
Cost of tagging
Anti-counterfeiting tags are inexorably
expensive and an enormous number of tags needs to
be attached to all agri-products. The risk of tags
being swapped cannot be avoided even after this
high cost is incurred.
Usability of verification
As inspecting tags and products require special
devices or skilled staff, only limited numbers of
products on the global market can be checked.
Consequently, counterfeiting is rarely discovered.
Consumers are not only unaware of anti-
counterfeiting measures but they do not want to pay
for these.
A novel method is required to solve these
problems so that agri-products can be authenticated
by anyone, anywhere, and at any time without
having to rely on costly tags or inspection
procedures.
1.3 Agri-Biometric Authentication
We propose a new methodology in this paper to
identify individual agri-products by having single
photographs taken of rind patterns (e.g., net, stripe,
and dot patterns on the rinds, see Figure 1) and by
matching these to an image database of
authenticated products. Since methods of
authenticating people using facial and fingerprint
features are called 'biometrics', we have called our
proposed method 'agri-biometrics'. The new method
authenticates the fruit bodies themselves through the
use of rind patterns, without the need to attach tags.
Rind patterns of fruit are generated depending on the
environment in which they are grown, and these are
unique to individual fruit. Even if fake fruit are
grown from the same seed and with the same
method of cultivation, creating an identical rind
pattern is supposed to be impossible. Thus, fake fruit
cannot be cultivated, at least not within reasonable
costs that would offset the expense of counterfeiting.
The key feature of the proposed method is that
only a single photograph is required that is taken
with handy standard cameras such those in mobile
phones to authenticate the individual fruit on hand
from the enormous amount of fruit on the market.
Producers in practical traceability systems register
images of shipped fruit into a database. As many
producers adopt automated systems for grading and
inspecting the quality of fruit (Kondo, 2010),
capturing images of individual fruit in a database
can easily be automated. If a traceability service to
match images with those in the database is provided
over the Internet, anyone can authenticate fruit using
his/her smartphone from everywhere and at any
time. As the whole market is monitored by everyone
at all times, counterfeiting is expected to be
effectively suppressed. Furthermore, as consumers
are able to check the products themselves, they
actually feel it is worth paying for added values.
Figure 1: Rind patterns of netted honeydew melon, water
melon, and green apple.
1.4 Previous Study and Proposed
Architecture
In the literature, a similar approach has been
reported. It identifies individual apples using
appearances of multiple images (Niigaki, 2009).
Since it requires numerous images to be taken for
each authentication to compensate for different
poses of apples, it is far from being a practical
application. We propose a new method that
normalizes pose variations to achieve authentication
using only a single image, and that utilizes
fingerprint matching technology to achieve
extremely accurate authentication. Figure 2 outlines
our new approach.
In our proposed method, a 3D model (sphere for
melons) approximates a fruit’s average shape to an
image and cancels out rotations in depth. This
simulates the same process as that with
fingerprinting, which also involves patterns on
curved 3D surfaces that are flattened onto a scanner;
the scanned image of the fingerprints does not
contain deformation due to rotations in depth.
The rind patterns of fruit differ greatly from
MELON AUTHENTICATION BY AGRI-BIOMETRICS - Identifying Individual Fruits using a Single Image of Rind
Pattern
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