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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.
Recognising 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 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 resolution; they are affected by different lighting conditions so they are inconsistent. We need to put the data in a form that means it can be shared meaningfully – so if you are barred from the Empire in central London, and you walk over to a casino in Curzon Street, they can also identify you even though the conditions are very different for Playerbook. To do that we have to dial out the inconsistencies due to CCTV – the angle of incidence to the target, lighting, general casino layout and how people flow through and pass the CCTV system – they’re all different in every casino. A reliable capture in one casino couldn’t really be used on the watchlist for another casino, because you’ll never be able to recreate the lighting in that original picture. That’s why we say this is a non-CCTV-based system, it’s working in infra-red so it’s light immune. It works as well in a bright sunny environment as it does in a darker casino floor.
CI: What is the fail rate of the system compared to humans?
GJ: The accepted ‘fail rate’ of humans according to a 2006 study is interesting. If we have 10 images of a person and that person is actually in the room with us, we are incorrect in matching an image to that person 25 per cent of the time. We’re incorrect one in five times when we only have two photographs[Editor’s note – that’s a 20 per cent fail rate with only two images, and 25 per cent with 10 images. People are strange]. The University of Glasgow followed that up with a study where the error rate is one in four when we try to match across different races – so if you have a staff of Western Europeans trying to identify Asian players, we’re wrong one in four times and vice versa.
CI: So how accurate is Playerbook? It surely cannot be 100 per cent accurate?
GJ: We’re not saying that it is. The first proper comparative study we did that involved any ground truth analysis was at the Sportsman Casino in Marble Arch in 2010. What we found was the accuracy of the Playerbook system was almost identical to humans. We picked a study group of 100 customers at the casino, all of whom smoked – thanks to the smoking ban in the UK, they went in and out of the door more frequently than other customers. We asked the receptionist that every time she saw one of the customers from this watch list she mark it down on a click sheet and we collated the information at the end of the working day. What we discovered was 137 detections on the watch list, and out of those 137 detections we had 136 marked by reception – so nominally, the system was up by one. But then we found the errors both reception and the Playerbook system had made, and they were almost exactly matched. We had 36 detections by the receptionist that were not seen by the sensor, and 37 detections made by Playerbook that were not seen by the receptionist.
CI: So the strength of this could be in combining this with human detection?
GJ: In an ideal world, perhaps. But with budgets declining in many organisations, this is about taking as many human beings out of an equation as possible.
Because the Sportsman trial was for ground truth analysis, we had a real-time CCTV system recording in reception for the duration, so we could go back to each instance the Playerbook system failed to spot an individual, and work out why. The main reason, it turned out, was people walking into the building behind other people. What that indicated to us was that we needed to move the sensor, so we could separate the faces better as they move past the sensor.
CI: So now you know the optimum configuration for success?
GJ: We redeployed it at the Empire and put it at the bottom of a staircase, so when anyone came down the stairs, they got nicely separated. We could then pick them off much more accurately. During that trial, because there is no full-time receptionist at the Empire as they have an open-door policy, we used the staff as the basis for the watchlist. In the two week trial, there were nearly 109,000 activations on the staircase leading to the gaming floor. From that, we had a watchlist that was only populated by 58 people. On every single day bar one, when the staircase was roped off because of maintenance work, we were getting positive identifications. We had no false identifications during that trial period.
CI: Presumably there’s the means to integrate this with a player tracking system or backoffice?
GJ: That’s exactly where we’re going with this at the VIP level. We’re already in technical discussions with one casino’s back office provider because this has great potential in terms of identifying high rollers or consistent customers. It’s also great for people counting, which is incredibly useful in a casino with an open-door policy.
