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Posts Tagged ‘face recognition’

Google Play Statistics Shows Ease of Use Correlates to Biometric Ratings

December 8, 2015

I saw an interesting press release titled “EyeVerify Gets Positive Feedback From Curious Users”. I know this company as a fellow biometrics vendor selling into some of the same markets as Sensory. I also knew that their Google Playstore rating hovered around a 3/5 rating while our AppLock app hits around a 4/5 rating, so I was curious about what this announcement meant. It made me think of the power of all the data in the Google Playstore, and I decided to take a look at biometric ratings in general to see if there were any interesting conclusions.

Here’s my methodology…I conducted searches for applications in Google Play that use biometrics to lock applications or other things. I wanted the primary review to relate to the biometric itself, so I excluded “pranks” and other apps that provided something other than biometric security.  I also rejected apps with less than 5,000 downloads to insure that friends, employees and families weren’t having a substantive effect on the ratings. I ran a variety of searches for four key biometrics: Eyes, Face, Fingerprint and Voice.

I did not attempt to exhaust the entire list of biometric apps, I searched under a variety of terms until I had millions of downloads for each category with a minimum of 25,000 reviews for each category. The “eye” was the only biometric category that couldn’t meet this criteria, as I had to be satisfied with 6,884 reviews. Here’s a summary chart of my findings:


As you can see, this shows the total number of downloads, the total number of apps/companies, the number of reviews and the avg rating of reviews per biometric category. So, for example, Face had 11 applications with 1.75 million total downloads and just over 25,000 reviews with an average review rating of 3.89.

What’s most interesting to me about the findings is that it points to HIGHER RATINGS FOR EASIER TO USE BIOMETRICS. This is a direct correlation as Face comes in first and is clearly the easiest biometric to use Voice is somewhat more intrusive as a user must speak, and the rating drops by .16 to 3.73, though this segment does seem to receive the most consumer interest with more than 5-million downloads. Finger is today’s most common biometric but is often criticized by its 2-hand requirement and that it often fails, requiring users to re-swipe, consumer satisfaction with fingerprint is about 3.67. Eye came in last, albeit with the least data, but numbers don’t lie, and the average consumer rating for that biometric comes in at about 3.42. If you consider the large number of reviews in this study and the narrow range of review scores (which typically range from 2.5 to 4.5), the statistically significant nature becomes apparent.

The results were not really a surprise to me. When we first developed TrulySecure, it was based on the premise that users wanted a more convenient biometric without sacrificing security, so we focused on COMBINING the two most convenient biometrics (face and voice) to produce a combined security that could match the most stringent of requirements.


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.