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VISIMETRICS PLAYERBOOK Visimetrics’ Playerbook:

a game changer for player ID? Player recognition is a vital part of any operation, from identifying cheats to recognising your best customers. Yet there isn’t a really robust tool to help operators with this fundamental – though Visimetrics may have a game changer with its Playerbook system.


Gary James, Visimetrics’ Business Development Director

ecognising your players – problem or otherwise – is often done with a combination of staff expertise and CCTV, which is actually, research shows, a fallible system with a consistent 20 to 25

per cent fail rate in recognition. Visimetrics say they have the solution which, if implemented correctly, can give a vast improvement – and is not CCTV- based. Gary James, Visimetrics’ Business Development Director, explains.

Casino International: Tell us about Playerbook

– why is it needed? Gary James: The problem we’re addressing is that

there are several categories of individuals the casinos would like to know about when they’re on the premises; they broadly divide up into people that have been barred for carrying out cheat moves or theft from the property, so there’s a primary security category; there’s also, though this varies from territory to territory, if you have a gambling problem you can self-exclude yourself from casino premises. It’s a big regulatory problem for a casino operator if a person who is self-excluded is found on the premises. They can be very hard to spot because of course they don’t really come up and introduce themselves to reception. The third category they would like to know about is VIPs, for obvious marketing reasons. The backdrop to the idea of Playerbook really is that

where possible, operators would like to open up to a 24- 7 operation where you can just walk in off the street. The way people come in to the premises and normally interact with reception staff, is falling away and it’s becoming much more open. So the ability to identify someone before they go on to the gaming floor is becoming harder, not easier, using human eyes because they’re not being used in the same way any more. Casinos now use a combination of image grabs from CCTV, and membership photos where they have them – and they collect all of these manually into a gigantic

48 NOVEMBER 2011

file of faces, with whatever information they know about that individual. They use this as a basis for the security staff to know who they might not want on the playing floor. If we can automate that process, there is a significant security benefit and a regulatory benefit as far as those self-excluders go – not to mention the marketing benefit for those VIPs. Some operators have turned to CCTV-based facial

recognition to try and make this work for them, and basically it hasn’t; sometimes, operators have trialled more than one software package, and switched them off because they’re not reliable enough. For us, our Playerbook solution is about recognising why those facial recognition systems aren’t robust enough for the task, and overcoming that problem. The way we’ve done this is to use a highly-specialised Playerbook sensor, which is not CCTV-based and therefore does not suffer from the problems that make a CCTV system unstable.

CI: There are many variables and inconsistencies when using CCTV – variable light, headgear, facial hair – it’s no wonder it can be flawed; using it for identity management must be difficult.

GJ: You’re right. There’s a huge amount of research

around how hard it is even for us as humans to identify other humans, based on even having a person standing in front of you and three or four different photographs of that person. The problems for CCTV in terms of facial

recognition are, firstly, the pictures are relatively low

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