Age assurance

Image of a hand holding a mobile phone which says "sensitive content" on the screen. The accompanying text reads "Online Safety Act - United Kingdom".

Understanding age assurance in the Online Safety Act

The Online Safety Act 2023 is a piece of UK legislation that aims to protect children and adults online. It covers a wide range of issues including minimising the risk of children seeing harmful and age-inappropriate content, removing illegal content like child sexual abuse material (CSAM), criminalising fraudulent and scam ads, and introducing age verification for certain online services. This blog looks at some of the age requirements in the Online Safety Act and what this means for tech companies, adult sites, gaming companies, social media platforms and dating sites.   What is the purpose of the Online Safety

11 min read
A man placing his face in the frame to perform a facial age estimation with Yoti

How accurate is facial age estimation?

“How accurate is it?” is the first question regulators, businesses and users tend to ask about facial age estimation. To date, we have mainly presented the technology’s Mean Absolute Error (MAE) as a proxy for accuracy. It’s an intuitive way to understand how accurate a model is. We can say it’s accurate to 1.3 MAE for those aged between 13 and 17 years or 2.5 MAE for those aged between 6 and 70 years. However, the answer is slightly more complicated. Following the COVID-19 pandemic, many people will be more aware of the terms ‘true positive’ and ‘false negative’

5 min read
An image of man with an 'over 18' facial age estimation credential. Around him are icons representing the gambling, gaming, financial services and retail industries.

How Yoti’s facial age estimation is used across different industries

Checking users’ ages has never been more critical for businesses catering to diverse audiences. However, they’re faced with the challenge of effectively verifying the ages of their users whilst maintaining seamless and user-friendly experiences.  Yoti’s facial age estimation is a secure, privacy-preserving way to do just that. Our technology is used across a variety of industries, both online and in-person. This includes retail, social media, dating, gaming, gambling and financial services. In this blog, we explore how businesses are using facial age estimation to create safer, more positive experiences for their users.   What is facial age estimation? Facial

10 min read
An image of the Instagram icon with the words "Teen Accounts" written around it. To the bottom left of the image are five icons: a padlock, a photo and video icon, two people, a messaging icon and a clock.

Helping Instagram to create safer online experiences with new Teen Accounts

From today, Meta is introducing new ‘Teen Accounts’ on Instagram for users under the age of 18. This change aims to help parents keep their teens safe online, by including features that have built-in protections. These include the ability to set daily usage limits, restrict access during certain hours and monitor their child’s interactions, such as the accounts they are messaging and the types of content they’re engaging with on the platform. New users under the age of 18 are, by default, given the strictest privacy settings. Under the new guidelines, teens aged between 16 and 18 will be

2 min read
Image of woman's face being estimated for age, which is not the same as facial recongition

Why Yoti’s facial age estimation is not facial recognition

There’s quite a bit of confusion about the differences between facial age estimation and facial recognition. While both types of technology work with images of faces, they’re used for different reasons and are trained in different ways. To help clear up some of these misconceptions, we’ve explained some of the key ways that our facial age estimation is not facial recognition.   Facial age estimation vs. facial recognition: designed to give two different outcomes. Facial age estimation delivers an estimated age result. Facial recognition delivers a match (or no match) between images of a person. [vc_column_text

9 min read
Yoti's Facial Age Estimation results versus the NIST Age Estimation evaluation report

Why do Yoti facial age estimation results published by NIST differ to those reported by Yoti in its white papers

In September 2023, we submitted our facial age estimation model to the US National Institute of Standards and Technology (NIST), as part of a public testing process. This is the first time since 2014 that NIST has evaluated facial age estimation algorithms. NIST age estimation reports are likely to become a globally trusted performance guide for vendor models. NIST assessed vendor Facial Age Estimation models using 4 data test sets at certain image sizes: NIST provides some example images: NIST note in their report that age estimation accuracy “will depend on

4 min read