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Face Match

What is Face Match technology?

Face match technology, also known as face recognition technology, compares an image which contains a face to one or more other images that also have faces within them. Utilizing deep learning, face match technology is then able to establish whether the faces in the two images are likely to belong to the same person- i.e. whether they are considered a match.

The difference between Face Match and Face Analysis

Whilst often confused, face match and face analysis are two entirely different technologies. While face match compares two faces based on whether facial landmarks are similar using facial recognition, face analysis anonymously analyzes facial features and provides more extensive commercial insights on faces such as age, gender and even mood. Read more here.

Discover Face Recognition Technology

What is Face Match technology used for?

Face match technology, which can recognise, compare and verify faces within less than a second, is used for a wide range of purposes. These include the following:

Know Your Customer (KYC)

Financial service providers are legally required to enforce anti-money laundering (AML) measures. One of these measures is Know Your Customer, or KYC.

KYC requires that organisations know exactly who they are doing business with and how much of a risk those people are likely to pose. In this context, face matching is used to verify the identity of an existing or potential customer- in short, answering the question: is this person who they say they are?

Verification is usually carried out by using an AI algorithm to compare a selfie of the customer with the photo contained in the customer’s identity document. The algorithm then determines whether or not there is a match based on facial landmarks. This software can work either through the cloud or on a device.

Payment Verification

Mobile payments are increasingly popular, with 92% of European millennials outlining their choice to make such payments in a recent study. This, combined with the Payment Services Directive 2 (PSD2) coming into effect in the EU towards the end of 2019, has meant that biometric payment verification is becoming increasingly important.

PSD2 makes two-factor authentication mandatory for a wide range of customer-initiated payments, excluding those made in cash. In practice, this means that there is an increased need for biometric identity verification.

Face matching is a convenient method for payment verification, because customers can easily use their smartphones to take selfies that can be compared to their enrolled image that the payment provider has in their database. This not only reduces time, but improves customer experience and brand loyalty by doing so.

Face recognition can also be used for payments in frictionless shopping scenarios, where shoppers’ faces are scanned by facial recognition cameras as they leave a retail store. Goods can be tagged and tracked using RFID chips, and shoppers’ banking details can be stored along with their facial images in a database. Taken together, this means that shoppers’ bank accounts can be debited as they walk out of a store without needing to go through a traditional checkout process. This process is, again, of huge benefit to companies looking to streamline customer experiences and increase customer retention in a competitive market.

Preventing Identity Fraud

Identity fraud is still a commonplace issue, with over 900 cases of identity fraud involving several individuals reported in 2019 in Europe alone. In being able to detect, track and verify faces in video streams and images in real time, companies are able to do their part in preventing this.

Access Control

When it comes to access control, face matching technology is connected to a camera, which takes a snapshot of the person trying to gain access to a room, building, event, application, or device. The face matching technology then compares the snapshot to a database of faces of individuals who have been given clearance for access. If there is a match, access is granted.

One of the advantages of using face recognition matching in this context is that many people are already familiar with the technology, since face recognition is now used to unlock many newer smartphones as well as smart home security systems such as Ring and Nest.

A study has also shown that “three out of four frequent flyers in the U.S. favor the use of biometric facial recognition to identify both domestic and foreign travelers” – which shows that there is huge potential for using face recognition in the context of access control.

Face Grouping

Face Grouping is a term used to refer to the process in which face matching technology is used to group the same or similar-looking faces together. In a digital photo album, for example you could use face grouping to cluster all your selfies, all the photos of your best friend, and so on. In this way, face matching is already commonplace on a wide range of mobile devices.


Retail stores, airports, and other locations can use face matching technology to recognize when individuals who are on a list of known shoplifters, wanted criminals, or if error suspects enter the premises. This is a powerful example of how world-class face matching technology can work quickly and effectively to solve big global issues.

Companies are enhancing their surveillance capabilities by adding anonymous audience measurement and real time face blurring to existing networks. Leveraging the return of their investments in hardware solutions.

How Face Analysis can power Face Match solutions

Sightcorp uses their proprietary and cutting-edge face detection and facial analysis software to assist in ID verification and KYC processes by supplying age and gender recognition. As well as providing face blurring solutions to companies that have surveillance cameras deployed but who also need to blur faces in real-time or after a fact due to privacy legislations. Due to the strict privacy laws around facial recognition that are enforced in many countries worldwide, face analysis is the best solution to get businesses the data that they need while also respecting customers’ right to privacy.

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