>> Male Voice: From the Fairmont Hotel in the heart of Silicon Valley, it's the Cube covering when IoT Met AI: the Intelligence of Things brought to you by Western Digital. >> Hey welcome back here everybody. Jeff Frick with the Cube. We're in downtown San Jose at the Fairmont Hotel at a small event talking about data and really in IoT and the intersection of all those things and we're excited to have a little startup boutique here and one of the startups is great enough to take the time to sit down with us. This is Shaun Moore, he's the founder and CEO of the recently renamed Trueface.ai. Shaun, welcome. >> Thank you for having me. >> So you've got a really cool company, Trueface,ai. I looked at the site. You have facial recognition software so that's cool but what I think is really more interesting is you're really doing facial recognition as a service. >> Shaun: Yes. >> And you a have a freemium model so I can go in and connect to your API and basically integrate your facial recognition software into whatever application that I built. >> Right so we were thinking about what we wanted to do in terms of pricing structure. We wanted to focus on the developer community so we wanted tinkers, people that just want to play with technology to help us improve it and then go after the kind of bigger clients and so we'll be hosting hack-a-thons. We just actually had one this past week in San Francisco. We had great feedback. We're really trying to get a base of you know, almost outsource engineers to help us improve this technology and so we have to offer it to them for free so we can see what they build from there. >> Right but you don't have an opensource component yet so you haven't gone that route? >> Not quite yet, no. >> Okay. >> We're thinking about that though. >> Okay, and still really young company, angel-funded, haven't taken it the institutional route yet. >> Right, yeah, we've been around since 2013, end of 2013, early 2014, and we were building smart home hardware so we had built the technology around originally to be a smart doorbell that used facial recognition to customize the smart home. From the the trajectory went, we realized our clients were using it more for security purposes and access-control, not necessarily personalization. We made a quick pivot to a quick access control company and continue to learn about how people are using facial recognition in practice. Could it be a commercial technology that people are comfortable with? And throughout that thought process and going through and testing a bunch of other facial recognition technologies, we realized we could actually build our own platform and reach a larger audience with it and essentially be the core technology of a lot cooler and more innovative products. >> Right, and not get into the hardware business of doorbells >> Yeah, the hardware business is tough. >> That's a tough one. >> We were going to through manufacturing one and I'm glad we don't have to do that again. >> So what are some of the cool ways that people are using facial recognition that maybe we would never have thought about? >> Sure, so for face matching - The API is four components. It's face matching, face detection, face identification, and what we call spoof detection. Face matching is what it sounds like: one-to-one matching. Face detection is just detecting that someone is in the frame. The face identification is your one to act so your going into a database of people. And your spoof detection is if someone holds up a picture of me or of you and tries to get it, we'll identify that as an attack attempt and that's kind of where we differentiate our technology from most is not a lot of technology out there can do that piece and so we've packaged that all up into essentially the API for all these developers to use and some of the different ideas that people have come up with for us have been for banking logins, so for ATMs, you walk up to an ATM, you put your card in and set up a PIN so to prevent against fraud it actually scans your face and does a one-to-one match. For ship industries, so for things like cruise ships, when people get off and then come back on, instead of having them show ID, they use quick facial recognition scans. So we're seeing a lot of different ideas. One of the more funny ones is based off a company out in LA that is doing probation monitoring for drunk drivers and so we've built technology that's drunk or not drunk. >> Drunk or not drunk? >> Right so we can actually measure based on historical data if your face appears to be drunk and so you know, the possibilities are truly endless. And that's why I said we went after the development community first because >> Right right >> They're coming to use with these creative ideas. >> So it's interesting with this drunk or not drunk, of course, not to make fun of drunk driving, it's not a funny subject but obviously you've got an algorithm that determines anchor points on the eyes and the nose and certain biometric features but drunk, you're looking for much softer, more subtle clues, I would imagine because the fundamental structure of your face hasn't changed. >> Right so it's a lot of training data, so it's a lot of training data. >> Well a lot of training data, yeah. We don't want to go down that path. >> So a lot of research on our team's part. >> Well then the other thing too is the picture, is the fraud attempt. You must be looking around and shadowing and really more 3D-types of things to look over something as simple as holding up a 2D picture. >> Right so a lot of the technology that's tried to do it, that's tried to prevent against picture attacks has done so with extra hardware or extra sensors. We're actually all cloud-based right now so it isn't our software and that is what is special to us is that picture attack detection but we've a got a very very intelligent way to do it. Everything is powered by deep learning so we're constantly understanding the surroundings, the context, and making an analysis on that. >> So I'm curious from the data side, obviously you're pulling in kind of your anchor data and then for doing comparisons but then are you constantly updating that data? I mean, what's kind of your data flow look like in terms of your algorithms, are you constantly training them and adjusting those algorithms? How does that work kind of based on real time data versus your historical data? >> So we have to continue to innovate and that is how we do it, is we continue to train every single time someone shows up we train their profile once more and so if you decide to grow a beard, you're not going to grow a beard in one day, right? It's going to take you a week, two weeks. We're learning throughout those two weeks and so it's just a way for use to continue to get more data for us but also to ensure that we are identifying you properly. >> Right, do you use any external databases that you pull in as some type of you know, adding more detail or you know, kind of, other public sources or it's all your own? >> It's all our own. >> Okay and I'm curious too on the kind of opening up to the developer community, how has that kind of shaped your product roadmap and your product development? >> It - we've got to be very very conscious of not getting sidetracked because we get to hear cool ideas about what we could do but we've got our core focus of building this API for more people to use. So you know, we continue to reach out them and ask for help and you know if they find flaw or they find something cool that we want to continue to improve, we'll keep working on that so I think it's more of a - we're finding the developer community likes to really tinker and to play and because they're doing it out of passion, it helps us drive our product. >> Right right. Okay, so priorities for the rest of the year? What's at the top of the list? >> We'll be doing a bigger rollout with a couple of partners later on this year and those will be kind of our flagship partners. But again, like I said, we want to continue to support those development communities so we'll be hosting a lot of hack-a-thons and just really pushing the name out there. So we launched our product yesterday and that helped generate some awareness but we're going to have to continue to have to get the brand out there as it's now one day old. >> Right right, well good. Well it was Chui before and it's Trueface.ai so we look forward to keeping an eye on progress and congratulations on where you've gotten to date. >> Thank you very much. I appreciate that. >> Absolutely. Alrighty, Shaun Moore, it's Trueface.ai. Look at the cameras, smile, it will know it's you. You're watching Jeff Frick down at the Cube in downtown San Jose at the When IoT Met AI: The Intelligence of Things. Thanks for watching. We'll be right back after this short break.