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If you’ve been on the internet at all, you know that there are hundreds of websites dedicated to helping people identify fake IDs. These are the sites that take your picture, do a little facial analysis, then present you with an ID that is either patently fake or likely not yours to begin with. They provide vital information about what might seem like a small problem but can quickly become big one if it’s not dealt with correctly. There are two main techniques to analysis. The first is called template matching, which is relatively simple in theory, but complex when you start considering the variety of ID cards available. The theory behind template matching is that if I know what my face looks like, then I can see what is supposed to be there in my photo, and then discern whether or not anything is missing. If I was to take a picture of your face using my cell phone camera and then compare the photo on your id card with it, I could tell with some certainty that something was wrong. This technique only works poorly because it's very difficult for most people to accurately recreate their own facial features from memory or based on a single photograph. This is why this method of analysis is not really used anymore. The second, and the current standard for fake-ID detection, uses machine learning to compare pictures from different angles and under different lighting conditions. These techniques have been adopted by the major sites which have been around for a couple years or so, because they are significantly more reliable than template matching's simple rules-based engine. Government documents in particular, like driver's licenses and passports, also tend to be asymmetrically distorted in a way that makes them difficult to fake well with a general face model. This technique has largely been commoditized by the market of fake ID review websites which use it in order to make money off their individual websites. In order for this technology to work, you need a large amount of data to train the model. Generally, this inputs to a computer will be a few thousand samples of "normal" face images. The quality of these images is also a big factor in the quality of the output. Photos taken with cell phones vary significantly in quality, and photos from passports and id cards will often be much more pixelated and distorted than photos from other types of sources. In return for providing you with this valuable training data, these sites charge anywhere from $150 to $650 or more for each ID they perform review on. The main reason why this technology is so highly sought after is because, as mentioned above, it is very easy to fake a picture using a template. That being said, almost anyone can take a simple photo of their own face and then manipulate it in a few different ways to make it look like one of several different photos that they've seen before. So it's not the method that's the problem – it's that there are many people who can easily produce fake IDs – so people will always have to rely on companies like Four Corner Studios to do their legwork for them. One final note about this technology: as explained above, these techniques use machine learning for facial analysis. cfa1e77820