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When Did Snapchat Facial Recognition Start

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Snapchat is actively thriving in the social media market with over 332M active daily users and >13M MAUs (monthly active users). The platform has skyrocketed its revenue by over 60% in 2021, hitting the number of $4.1 billion when the vendor released tons of brand-new facial recognition-based capabilities like 3D masks and trailblazing real-time avatars.

These numbers motivate multiple startups to create Snapchat-like experiences in their self-developed applications to gain the same level of success. However, many brands still wonder how the platform’s facial recognition technology works and if it’s worth investing in.

So, this post will guide you through Snapchat’s technology-driven journey by featuring:

– How and why Snapchat AR filters evolved

– What technology is behind Snap chat filters

– How Snapchat facial recognition works

– 8 best Snapchat filters.

History of Filters on Snapchat: How They Evolved

Snapchat was among the first and only adopters of augmented reality-powered technologies by introducing never-seen-before professional-looking AR filters in 2015. They have gone viral in a matter of weeks as new avant-garde AR-enabled filters have fully changed the way users interact virtually.

Later on, Snapchat released Lens Studio in December 2017 which streamlined the way users and advertisers could create tailor-made filters for applying them to personalized snaps or even sponsored content. This boomed the number of daily and monthly active users as advertisers have started leveraging all-in-one Snapchat-centric AR marketing campaigns to skyrocket their sales initiatives.

Next, the platform has released a new update that allowed end-users to augment world-known landscapes, human bodies, hands, and even pets with immersive built-in content. For example, the “Ground Transformation” AR-powered filter now empowers users to transform floors into lava in real-time. Forbes has stated that this feature would become a trendsetter and a top-1 priority for brands as they can now tune any terrain and transform it into a brand-enabled landscape.

Up to date, Snapchat AR Lenses are becoming a full-featured digital economy and social environment facilitating higher user engagement and driving lead generation results for brands. For example, the vendor has launched an AR filter marketplace that allows content creators to produce never-seen-before augmented reality effects and trade them to the platform’s community.

The technology behind Snapchat filters

The first release of Snapchat AR filters was in 2015 when the company acquired Looksery, a Ukraine-based computer vision startup that released an AR-powered app for virtual chatting, for $150M. This has enhanced the in-house technical team of a brand and led to building over 3,000 augmented reality in-app filters for end-users. This marked the beginning of modern-day social media filters and masks.

The AR-enabled social media app has started thriving in the global business landscape so that dozens of companies, celebrities, influencers, and top-tier corporations have begun evolving their brands or self-developed products with new augmented reality opportunities.

For example, Jessica Alba and Ariana Grande have joined the platform to acquire new audiences and boost their digital presence by applying real-time 3D dog masks, bread faces, or golden goddess lenses. The never-seen-before use case happened during the 2016 Oscar Awards ceremony when most celebrities started applying Snapchat “Face Swap” filters to switch their face with Leonardo Di Caprio and support the non-Oscar artist.

As Snapchat’s AR-powered application evolved in the industry, so did the augmented reality facial recognition technologies behind the filters and effects. Today, neural networks and computer vision algorithms help retail, cosmetics, e-commerce, beauty, live streaming, healthcare, and even automotive market-leading companies streamline their in-house staff training, boost ads campaigns results, minimize product return rates, and skyrocket sales.

Snapchat facial recognition and tracking technology

The platform utilizes face recognition and tracking technologies that match identify human faces as 1s or 0s that correspond to multiple facial areas stored in large databases. They include eyebrows, noses, foreheads, and others.

The algorithms utilize the following pipeline: first, they identify different facial elements marking them with 1s or 0s that represent lighter or darker areas. Second, networks process hundreds of coordinates to spot repeating elements while scanning the camera image. This helps computing technologies distinguish faces from other objects on an image.

But here comes a question: how Snapchat adds filters to our faces that accurately? Here comes the Active Shape Model (ASM) – a model-based method that processes prior models of predicted image content to attempt to detect the best possible match by comparing data to new image content.

Simply put, computing algorithms process all found facial points to decide which ones belong to ears, eyes, nose, and others. As a result, computer vision technology builds a 3D mask model that can be easily scaled, rotated, or adapted if new input data comes from a user’s camera.

In the same way multiple brands leverage AR technologies with Banuba to empower their internal products or market-facing solutions with immersive experiences. They include 1,000+ real-time augmented reality filters, try-on technologies, virtual makeup solutions, and more.

Summing Up

We hope our post helped you get the answer to when did Snapchat facial recognition start and provided a full-featured technology-driven journey the platform has surpassed to become a world-leading platform.

The case of Snapchat has shown that modern trailblazing AR-powered technologies provide benefit-oriented and customer-centric capabilities for brands to integrate, customize, and improve on. This is backed up by multiple Snapchat-like clones emerging yearly to repeat the success and gain tons of users through facial recognition and tracking opportunities.

 

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