Subtype ID And Anti Counterfeit Software- Any Interest?

You definitely have your work cut out for you. Good luck with your project.

If the coin type software had "compare-to-pictures" of all available die marriage possibilities, it definitely could work!

And!

Not only would the software detect counterfeits, but it would also automatically ID the Newcomb, Sheldon, VAM, Overton, etc of the coin!!

I'd invest in that!

Very interesting approach. I'm a computer scientist and programmer and have thought about this but was seeing lots of tech problems. However, those of us in technology are expected to solve those problems and so I admire your enthusiasm.

Having the images of all possible varieties is a start. However, I wonder about the condition of the coin you are trying to validate. If a key component of identifying the coin is worn (and doesn't match) - how will the algorithm distinguish that from a fake component?

This looks like a huge recursive algorithm. Please keep us in the loop on your progress.

I think the only problem with this is you would have to have every known variety listed because on some coins the design does look a little different in places and may show up and make you think its counterfeit when in fact its not, its just a different variety of the same type and maybe even the same date. But any tool a person can use that will educate them is a good thing though

This is a magnificent idea that I've toyed around with a bit myself using scripted GIMP plugins and node.js.

However, after a lot of effort, the biggest problems I came across were with lighting, toning, keystone/orientation, and wear.

Out of all of those problems, keystone and orientation is the easiest thing to fix as there are orientation and frame detection algorithms out there that work pretty well. The most fun I've messed with is that I trained up a neural network to recognize not just the orientation of a coin, but the type of coin (just with modern Pennies, Nickels, Dimes, and Quarters [obverse] -- State Quarter reverse recognition would have to be its own network, ugh..).

Outside of that, straight image comparison math is very limited. What you'd really need to do is take a heightmap of the actual coin (with a high-def stereo camera or laser scanner) and then you can do a height subtraction with noise reduction, and perhaps develop some sort of wear transformation model to narrow down what areas to focus your comparisons on.

In the end it'd need to be more like a fingerprint recognition program where known points of interest are the main focus of comparison.

In any case, good work so far! :-)

One of the most powerful ways to detect a fake is to superimpose a picture of the suspected fake over a picture of an equivalent known genuine coin.

If there are differences, they show up very obviously.

A fake detection software of this type could me marketed for the benefit of ALL collectors.

Sometimes it's helpful to borrow from what we've learned in solving other complex tech problems. The comment made me revisit an algorithm that makes the decision to discard the billions of spam emails every day. There is a remarkable similarity between the method of detecting spam emails and the detection of fake coins and the determination of the coin's specific variety.

If a human were filtering the emails it would be a judgment decision based on the content of the email and the person's experience. This is very similar to a human who examines coins to detect fakes. A programming algorithm can't ‘think' so instead it often digitizes the results into a 1 to 100 scale where 100 means ‘absolutely sure' and 0 means ‘ no idea'.

Spam software is typically a heuristic algorithm which almost sounds like a contradiction of itself. It makes decisions and then ‘learns' how to make better decisions by repeating itself. Each decision carries a weight depending on how important it is. For example if spam software sees ‘buy Rolex cheap' or ‘offshore pharmacy' in an email it would find that to be adequate proof to immediately assign a 100 rating to the email and delete it. If a fake coin detection program sees a coin of a certain date that should have 13 stars and it only has 12 stars then a score of 100 is immediately assigned because the coin is a fake.

This can also apply to determining the variety of a coin. Let's use Capped Bust half dollars as an example. Several of the years have over 20 different varieties. There are dozens of diagnostics on the obverse like the position of the stars to the milling and on the reverse positions of the ‘A's with respect to the milling. Some diagnostics help us get closer to the answer more quickly than others -- like the alignment of the ‘I' of PLURIBUS to the ‘T' of STATES. This one diagnostic often narrows the number of possible varieties from 20+ down to just a few. This diagnostic would carry a bigger weight factor than some of the minor diagnostics in the algorithm. I often use this web site first before opening my Parsley book. http://www.wix.com/coinzip/bust-hal...tribute/1828

OK, back to the problem of worn coins that I raised in my earlier post. Each coin series is different but the major/minor comparison still applies. On capped bust coins the I to T diagnostic is usually available unless the coin is in poor condition. Some of the diagnostics that point to the milling may not be available because of the wear on the edge of the coin. So, a missing diagnostic involving the milling should not trigger a suspicion of a fake coin if other diagnostics can be used to determine the variety.

This is a very complex process but -- good news --it's all documented very clearly.