FaceAuthMe – Low friction facial biometric authentication

Authentication is a hot topic more than ever covering many aspects of our lives. It’s closer to our lives and even penetrates our most personal devices. With enough evidence that traditional methods are not enough to provide a safe, trusted authentication, there’s a need for an innovative solution that can bring extreme low friction to the end consumers.

Authentication is a hot topic

Authentication is growing to be a hot topic and an integral aspect of our daily routines, even penetrating personal aspects. Everyday, we enter passwords multiple times to turn on the laptop, use fingerprints to log in to mobile apps, receive One Time Passwords (OTP) to verify transactions. Authentication is everywhere and the need to authenticate ourselves safely and with trust , exists as fraudsters are increasing at an alarming rate.

Fraudsters are increasing at an alarming rate

Fraudsters are getting smarter everyday. From OTP hijacking, device spoofing to sophisticated social engineering schemes, they outsmart the technology. Unauthorized financial fraud losses in the UK alone across payment cards, remote banking and cheques totalled £824.8 million in 2019 while the combined worldwide figures for credit card fraud stand at an alarming $27 Billion. These circumstances demand that fraud is prevented at the earliest of customer interaction and customers are protected by better authentication methods.

Passwords are unreliable

  • Passwords are the root cause of over 80% of data breaches
  • Up to 51% of passwords are reused
  • The average user has more than 90 online accounts and mostly forget passwords
  • 1/3 of online purchases are abandoned due to forgotten passwords
  • 50% of users abandon their online banking transactions due to the hassle and friction caused by passwords
  • $70 average help desk labor cost is spent for a single password reset

(Source FIDO alliance)

One Time Passwords (OTP) are not the answer

  • SMS is not reliable. NIST strongly recommends against its use
  • Cost is higher
  • Increasing social engineering OTP scams

In device biometrics data are not secure enough

  • Dependence on device manufacturers for sensitive authentication solutions is too risky
  • Vulnerabilities of the large range of devices expose doors for fraudsters to break in
  • Many financial regulators are strongly against using in-device biometrics
  • Depending on the capabilities of the user device is a too much compromise

The world needs stronger, friendlier authentication

Mandates such as 3D Secure 2.0’s requirement for 2 factor authentication (2FA) will drive merchants and banks to adopt biometrics to make the payment experience smoother across a variety of platforms. Ubiquitous biometric sensors, present in many mobile devices, are in the forefront driving this wide adoption of biometric authentication. Yet, a widely unspoken, far unseen problem stands in the way of secure authentication. Can we extensively depend on in-device biometrics when it comes to crucial authentication processes?

Advantage of FaceAuthMeTM

Zone24x7’s facial biometrics solution FaceAuthMeTM is a secure, low friction solution that addresses the needs for Strong Customer Authentication (SCA) now and for the future. FaceAuthMeTM uses the person’s face to authenticate access using any device’s camera. This is done quickly, seamlessly, and a low-friction high secure method. FaceAuthMeTM uses sophisticated machine learning and AI algorithms that capture intelligently facial biometrics and other data of the customer to uniquely identify a genuine customer from a fraudster. FaceAuthMeTM provides a one-stop, seamless authentication experience for all authentication needs.

The novelty and innovative edge

No dependency on customer’s mobile phone

While most existing biometric authentication solutions heavily rely on the capabilities of customers’ mobile phones, FaceAuthMe is an off-the-device solution. There is no dependence on device manufactures, brands or 3rd party software vendors. Further, this enables FaceAuthMe to run on devices with extremely low processing footprint

Device agnostic, OS agnostic

FaceAuthMe is completely device agnostic, where it works on any device with a camera and an internet connectivity. The mobile phones, tablets, laptops or even desktops can be easily utilized in the authentication process. Further, reaping the benefits of being device and operating system agnostic, FaceAuthMe can run on non-personal device use cases such as POS terminals, ATM machines etc.

No action required from the end user

A key element in biometric based authentication is to verify if the user is live (Liveness), as fraudsters can easily impersonate a genuine customer with images or videos. In order to check liveness, most competitive solutions ask the user to do something in front of the camera such as, smiling, winking, turning the head sideways etc. Advanced machine learning and artificial intelligence algorithms in FaceAuthMe make sure that no action is required at all for liveness verification, ensuring extreme low friction.

Authenticate with just a selfie

Taking a selfie is now standard and familiar to almost all the demographics. While most existing products depend on complex, data heavy inputs such as videos and collection of image bursts, FaceAuthMe innovates the customer experience by taking just a selfie. The in-house developed artificial intelligence/ machine learning algorithms are powerful in calculating if an end user is genuine or a fraudster, just by looking at a single image.

No software/app needed – FaceAuthMeTM is not a mobile app

Many biometric solutions, that boast about biometric authentication in the existing market, depend heavily on special software applications developed to achieve the purpose. This adds a friction factor to the end customer where software needs to be downloaded and installed. Research clearly says that the penetration of mobile phone based apps is quite low (about 30%) even if the customer has a suitable mobile phone. FaceAuthMe, being a browser based application, removes this need for installing software and authenticates seamlessly.

Privacy by design and by default

FaceAuthMe considers privacy of the end customers quite seriously. Usage of pseudonymisation of customer data and encryptions used at data transfers makes sure that FaceAuthMe follows data protection by design. Its strictest privacy settings are applied by default, without any manual input from the end user. Personal data is kept for a definite period of time and it’s privacy friendly to the end customer making FaceAuthMe follow data protection privacy by default.


Being a proud Sri Lankan, inhouse developed product, FaceAuthMe is changing the status quo of how authentication is done at most secure levels. It’s innovative approaches in providing world class facial biometrics help achieve a low friction authentication while ensuring the security aspects, thus having a clear distinction with its competitors.

Thilina Bandara

Tech Lead – Cognitive Machine Learning

Data Analytics, Machine Learning. A real business need?

There aren’t many people nowadays who would not have at least heard of the term “Artificial Intelligence” or AI, with all the frenzied media hype and the many romanticized and science fiction movies around it. AI has become a house-hold term though much less a reality despite all this hype.

The greatest impediment to AI to date has been the fact that programmers need to ‘program’ (a software) and for that software to perform a certain function. It cannot therefore think and act on its own. It will only do what it has been programmed to do and in response to certain circumstances, the so-called AI that scientists kept developing over the years could only do just that – respond to a specific set of circumstances based on what it was programmed to do. What AI could not do till now was to think on its own and respond to a new type of circumstance and to gather ‘experience’. This landscape though seems to be changing now and soon may be part of the history in pursuit of real Artificial Intelligence.

With the evolution of the ICT domain, the terms “Machine Learning” and “Deep Learning” have found their way into modern tech terminology with the discussions around their use spanning across Big Data Analytics, BI (Business Intelligence), and most importantly AI (Artificial Intelligence).

Artificial Intelligence and Machine Learning?

Think of a machine that can think and respond as a human being with zero defects in nature; it would be a perfect example of an AI in real life. Add an artificial consciousness to it to make it much more perfect.

Machine Learning itself is a form of AI, perhaps the most promising form of AI to date. This enables certain algorithms to read or observe and gather knowledge and experience much the way we do and learn and evolve to a certain extent and respond to certain situations through that self-learning without executing any pre-programmed queries like an ordinary application. This type of AI aims at the improvement of computational thinking that can self-learn and advance when new data or information is presented.

Such advanced technologies have improved or enhanced many activities that require human intervention to a level where no human involvement is required. For example, if you are using Google search that is powered by complex ML algorithms, the algorithms will enable Google to come up with new search signals and aggregations to provide an intelligent user experience that is personalized. (Google’s “RankBrain Algorithm”). This is actually in existence though it is not easy to believe and we take it for granted every time we do a ‘simple’ Google search. You will have noticed that it delivers more appropriate results within a split second: this is a ML-based AI in action behind all that apparent simplicity.

Deep learning on the other hand is very similar to Machine Learning, but with the difference that it’s designed to study and learn a very specific set of information more deeply and react more intelligently to that data. This difference between ML and DL is somewhat akin to a layperson and a trained professional doctor responding to an illness: one will look at it very generally and learn and react to a certain extent while the learning and response of the other one are at a deeper or more advanced level. The actual difference here is based on the algorithm behind the learning ability which will determine the extent of the learning and the relation.

With interconnected neural networks (a computer system sculpted basing the human brain and nervous system’s designs) and Deep Learning algorithms, Machine Learning has earned its way into modern-day business intelligence and data mining to enhance data analytics.

So how will all this affect us? Here’s how. Some examples of the applications of Machine Learning-based systems are:

  •  Improved AI – Vehicles are driving themselves with collision avoidance reducing the risk to human lives and autonomous Nano mites treating cancer cells in most complex systems in the human body such as the human nervous system.
  • Improved AI game playing – for years we are used to playing computer games with the computer as an opponent where most of its moves are predictable since they are preprogrammed. However, when an AI comes to the play backed with advanced ML the computer will be hard to predict and will give you that experience of a lifetime.
  • Complex data analysis – This will enable in-depth views of any kind of data sets regardless of the quantity or the complexity. Paving the way towards predictive and prescriptive data analytics with higher accuracy.
  • Developed InfoSec measures – there will be intelligent biometric access control which is so smart which will predict criminal behavior even before a crime takes place. (not as in the movie “Minority Report” though) We already see technologies such as “Cognitive Vision” being used to identify certain faces and objects through camera feeds.
  • Improved processes – Financial systems, banking systems, Accounting, or any data-driven system will be more efficient and with improved accuracy will give timely reports and predictions reducing any opportunity risk. Not only finance, we can expect legal court proceeding outcomes will also be predictive thanks to ML in the near future. Human judgement will easily be replaced by the machine soon.

By the time this article is getting published, you may have already witnessed the wonders these technologies can bring us. But that is just the beauty of it. Imagine if things go rogue? Yes, it will bring certain negative consequences as well. I let you take the judgement here. Businesses nowadays have already taken a step forward by adopting technologies such as Big data analytics, AI driven data science…etc. The visionary leaders behind such organizations foresee the value and the business impact such investments will bring them in the near future.

Here at Zone24x7, we work closely with such technologies building great customer centric products and services that empathize and resolve their most unique business challenges. With decades of experience in Machine Learning and Artificial Intelligence, Zone has produced a few key products/services that help any business to “Cross the chasm”;

  • The Analytics Center: A data platform to build, deploy, and manage big data solutions with AI-powered actionable advanced analytics
  • FaceAuthMe: A facial biometric authentication platform that uses advanced machine learning and AI algorithms to provide Strong Customer Authentication
  • SerendibAI: An artificial intelligence-powered cognitive vision platform that analyzes video footage to provide actionable insights
  • MATRIX24x7: An IoT driven remote monitoring and management platform that can be integrated with a range of external systems to give users centralized monitoring and troubleshooting capabilities

With these technologies any business organization will be equipped to deliver more efficient, reliable and experiential services to their clients. 

Are you ready to lead the next generation of the technology evolution of your business? It is your call now.

Thivanka Vithange

Senior Business Designer