In 2015, the social media landscape surpassed a never-seen-before market boom which even displaced the overwhelming success of Instagram. Snapchat’s AR-powered facial recognition and 3D effects came into play to revolutionize the industry for both users, content creators, brands, and advertisers.
Currently, the platform has over 332M active daily users and 13 million monthly active users (MAUs), along with a grown revenue by 63% in 2021, reaching a number of over $4B. These numbers inspire hundreds of startups to kickstart their own Snapchat-like application and reach the same success. But many brands still puzzle over how the product’s facial recognition technology works and if it’s viable and investment-worthy.
If this resonates with your use case, the post will help you explore the technology-driven success of Snapchat and find out how facial recognition capabilities function beyond the curtain.
Snapchat Technology-Driven Journey: How Filters Evolved
Snapchat was among the first industry-leading social brands to implement and release augmented reality-enabled solutions like AR facial filters and 3D masks. In 2015, the platform released a game-changing release that went viral in weeks, and this update revolutionized the way social brands serve their end-users.
In 2017, the vendor launched Lens Studio – a community-driven built-in hub for creating customized avatars, masks, and filters for tailor-made snaps or sponsored content. The new product feature has thrived in the social media market again, which facilitated the DAUs and MAUs growth along with acquiring a growing number of advertisers. They started joining the platform to kickstart all-in-one AR-powered ads and promo campaigns using the cutting-edge facial recognition technologies to grow user engagement and drive sales.
Later on, Snapchat launched a brand-new update that enabled users to digitize world-known landscapes, human body parts, and pets with immersive augmented reality objects. For instance, the “Ground Transformation” AR filter now enables users to change the way floors and grounds look by transforming them into lava. As the substitution effect may be customized, Forbes argues that this effect would become a game-changing and top-tier priority for industry-leading companies as they now run multiple marketing campaigns by transforming any terrain into customized brand-on landscapes.
Currently, Snapchat AR facial filters are no longer an entertainment-only initiative but a fully-fledged digital economy and ecosystem for most industry-specific enterprises. They can leverage the platform’s augmented reality capabilities to facilitate user engagement, lead generation, and branding enhancements. For instance, the platform has recently released an AR filter marketplace that helps brands and content creators generate custom AR effects and share them with the platform’s community on a paid or free basis.
Snapchat Facial Recognition: Success Timeline
The adoption of Snapchat’s facial recognition started in 2015 by acquiring Looksery – a Ukrainian computer vision startup. It had success after launching an AR-enabled virtual chatting application that brought a never-seen-before and innovative approach to digital communications. The $150M investment was a great value for money as the brand has significantly enhanced its in-house neural network engineering expertise and resulted in creating over 3,000 engaging AR facial filters for Snapchat users. This became a first step towards the adoption of facial recognition technologies – known today as social media filters and masks.
The technology-driven release started thriving in the social media landscape, attracting dozens of world-known enterprises, influencers, and celebrities to join the platform. They all were interested in leveraging augmented reality capabilities to grow their personal brand awareness and skyrocket sales initiatives.
For instance, Ariana Grande and Jessica Alba were among the early adopters of Snapchat facial recognition filters applying 3D dog masks, bread faces, and golden goddess lenses to acquire new audiences and improve their digital presence. More than that, the Oscar Awards in 2016 also featured Snapchat’s technologies as world-known attendees started applying a “Face Swap” filter to switch faces with Leonardo Di Caprio and support the artist in getting a long-awaited award.
Since the technology-driven market success of Snapchat, the computer vision-based facial recognition capabilities have evolved as well. Currently, ML-powered algorithms facilitate live streaming, healthcare, retail, e-commerce, manufacturing, and cosmetics brands to enhance employee training, shorten the go-to-market (GTM) period, minimize product return rates, and evolve ads campaign results.
How Snapchat Facial Recognition and Tracking Work
Since 2015, the product has been utilizing facial recognition and tracking algorithms that help identify human-based faces as specific points (1s or 0s) that correspond to specific areas of face analysis stored in a database. They may include lips, eyebrows, ears, foreheads, etc.
First, computer vision-based algorithms mark different facial elements by identifying their points (1s or 0s) that represent darker or lighter facial areas. Next, neural networks handle tons of coordinates to identify ever-repeating points while scanning the camera image hash. Here comes the AR-enabled magic, which enables tech teams to distinguish faces from other image-specific objects.
Second, algorithms should accurately apply filters and masks to moving or rotating users’ faces, which is no longer a problem due to Active Shape Model (ASM). ASM is a model-based approach to processing prior generated models of predicted image-specific content for further comparison operations applied to new image content. In simple terms, neural networks generate post-analysis 3D mask models after analyzing images’ content which facilitates the process of handling new input data from a user’s camera.
Multiple companies leverage AR-powered technologies the same way with AR SDK Banuba, which offers over 1,000 real-time 3D masks and avatars, virtual make-up try-on solutions, and more. These capabilities help brands empower their self-developed applications with technology-driven solutions and provide end-users with immersive augmented experiences.
