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Interesting Announcements from Alibaba, Google and Microsoft

March 23, 2015

This month had three very different announcements about face recognition from Alibaba, Google, and Microsoft. Nice to see that Sensory is in good company!!!

Alibaba’s CEO Jack Ma discussed and demoed the possibility of using face verification for the very popular Alipay.

A couple interesting things about this announcement…First, I have to say, with a name like Alibaba, I am a little let down that they’re not using “Open Sesame” as a voice password to go with or instead of the face authentication… All joking aside, I do think relying on facial recognition as the sole means of user authentication is risky, and think they would be better served using a solution that integrates both face and voice recognition (something like our own TrulySecure), to ensure the utmost security of their customers’ linked bank accounts.

Face is considered one of the more “convenient” methods of biometrics because you just hold your phone out and it works! Well, at least it should… A couple of things I noticed in the Alibaba announcement: Look at the picture…Jack Ma is using both hands to carefully center his photo, and looking at the image of the phone screen tells us why. He needs to get his face very carefully centered on this outline to make it work. Why? Well, it’s a technique used to improve accuracy, but this improved accuracy, trades off the key advantage of face recognition, convenience, to make the solution more robust. Also the article notes that it’s a cloud based solution. To me cloud based means slower, dependent on a connection, and putting personal privacy more at risk.  At Sensory, we believe in keeping data secure, especially when it comes to something like mobile payments, which is why we design our technologies to be “embedded” on the device – meaning no biometric data has to be sent to the cloud, and our solutions don’t require an internet connection to function. Additionally, with TrulySecure, we combine face and voice recognition, making authentication quick and simple, not to mention more secure, and less spoofable than face-only solutions. By utilizing a multi-biometric authentication solution like TrulySecure,  the biometric is far less environmentally sensitive and even more convenient!

Mobile pay solutions are on the rise and as more hit the market differentiators like authentication approach, solution accuracy, convenience and most of all data security will continue to be looked at more closely. We believe that the embedded multi-biometric approach to user authentication is best for mobile pay solutions.

Also, Google announced that its deep learning FaceNet is nearly 100% accurate.

Everybody (even Sensory) is using deep learning neural net techniques for things like face and speech recognition. Google’s announcement seems to have almost no bearing on their Android based face authentication, which came in the middle of the pack of the five different face authentication systems we recently tested. So, why does Google announce this? Two reasons: – 1) Reaction to Baidu’s recent announcement that their deep learning speech recognition is the best in the world: 2) To counter Facebook’s announcement last year that their DeepFace is the best face recognition in world. My take – it’s really hard to tell whose solution is best on these kind of things, and the numbers and percentages can be deceiving. However, Google is clearly doing research experiments on high-accuracy face matching and NOT real world implementation, and Facebook is using face recognition in a real world setting to tag photos of you. Real-world facial recognition is WAY harder to perfect, so my praise goes out to Facebook for their skill in tagging everyone’s picture to reveal to our friends and family things might not have otherwise seen us doing!

Lastly, Microsoft’s announced Windows Hello.

This is an approach to getting into your Windows device with a biometric (face, iris, or fingerprint). Microsoft has done a very nice job with this. They joined the FIDO alliance and are using an on-device biometric. This approach is what made sense to us at Sensory, because you can’t just hack into it remotely, you must have the device AND the biometric! They also addressed privacy by storing a representation of the biometric. I think their approach of using a 3D IR camera for Face ID is a good approach for the future. This extra definition and data should yield much better accuracy than what is possible with today’s standard 2D cameras and should HELP with convenience because it could be better at angles can work in the dark. Microsoft claims 1 in 100,000 false accepts (letting the wrong person in). I always think it’s silly when companies make false accept claims without stating the false reject numbers (when the right person doesn’t get in). There’s always a tradeoff. For example I could say my coffee mug uses a biometric authenticator to let the right user telepathically levitate it and it has less than a 1 in a billion false accepts (it happens to also have a 100% false reject  since even the right biometric can’t telepathically levitate it!). Nevertheless, with a 3D camera I think Microsoft’s face authentication can be more accurate than Sensory’s 2D face authentication. BUT, its unlikely that the face recognition on its own will ever be more accurate than our TrulySecure, which still offers a lower False Accept rate than Microsoft – and less than 10% False Reject rate to boot!

Nevertheless, I like the announcement of 3D cameras for face recognition and am excited to see how their system performs.

Biometrics – The Studies Don’t Reveal the Truth

May 7, 2014

If you read through the biometrics literature you will see a general security based ranking of biometric techniques starting with retinal scans as the most secure, followed by iris, hand geometry and fingerprint, voice, face recognition, and then a variety of behavioral characteristics.

The problem is that these studies have more to do with “in theory” than “in practice” on a mobile phone, but they never-the-less mislead many companies into thinking that a single biometric can provide the results required. This is really not the case in practice. Most companies will require that False Accepts (error caused by wrong person or thing getting in) and False Rejects (error caused by the right person not getting in) be so low that the rate where these two are equal (equal error rate or EER) would be well under 1% across all conditions. Here’s why the studies don’t reflect the real world of a mobile phone user:

  1. Cost is key. Mobile phone manufacturers will not be willing to invest in the highest end approaches for capturing and measuring biometrics that are used by academic studies. This means less MIPS less memory, and poorer quality readers.
  2. Size matters. Mobile phone manufacturers have extremely limited real estate, so larger systems cannot be properly deployed, and further complicating things is that an extremely fast enrollment and usage is required without a form factor change.
  3. Conditions are uncontrollable. Noisy environments, lighting, dirty hands, oily screens/cameras/readers are all uncontrollable and will affect performance
  4. User compliance cannot be assumed. The careful placement of an eye, finger or face does not always happen.

A great case in point is the fingerprint readers now deployed by Apple and Samsung. These are extremely expensive devices, and the literature would make one think that they are highly accurate, but Apple doesn’t have the confidence to allow them to be used in the iTunes store for ID, and San Jose Mercury News columnist Troy Wolverton says:

“I’ve not been terribly happy with the fingerprint reader on my iPhone, but it puts the one on the S5 to shame. Samsung’s fingerprint sensor failed repeatedly. At best, I would get it to recognize my print on the second try. But quite often, it would fail so many times in a row that I’d be prompted to enter my password instead. I ended up turning it off because it was so unreliable (full article).”

There is a solution to this problem…It’s to utilize sensors already on the phone to minimize cost, and deploy a biometric chain combining face verification, voice verification, or other techniques that can be easily implemented in a user friendly manner that allows the combined usage to create a very low equal error rate, that become “immune” to conditions and compliance issues by having a series of biometric and other secure backup systems.

Sensory has an approach we call SMART, Sensory Methodology for Adaptive Recognition Thresholding that takes a look at environmental and usage conditions and intelligently deploys thresholds across a multitude of biometric technologies to yield a highly accurate solution that is easy to use and fast in responding yet robust to environmental and usage models AND uses existing hardware to keep costs low.