September 18, 2022
Snapchat's Face Recognition Technology, YT Trending - Latest Technology News

Snapchat’s Face Recognition Technology a new era

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.

As of right now, the platform has over 332 million active daily users and 13 million monthly active users (MAUs), and its revenue is expected to grow by 63% in 2021 including over $4 billion. These statistics encourage numerous entrepreneurs to launch their own successful Snapchat-like applications. However, a lot of firms are still uncertain about how the face recognition technology in the product operates and whether it is practical and worth the cost. If this applies to your use case, the post will help you in learning more about Snapchat’s technological success and how face recognition technology actually works.

The Evolution of Snapchat’s Filters

One of the market-leading social media platforms to implement and make available augmented reality-enabled products like 3D masks and facial filters was Snapchat. The platform released a hugely important update in 2015 that transformed the way social brands interact with their customers and went viral in a matter of weeks.

Read More: Snapchat loses $10 billion as stock hits new 52-week low – India TV News

The vendor introduced Lens Studio in 2017 as a community-driven integrated hub for designing unique avatars, masks, and filters for customized photos or sponsored content. The new product feature has once more flourished in the social media market, which has helped in the growth of DAUs and MAUs as well as the recruitment of an increasing number of advertisers. They began utilizing the platform to launch all-encompassing augmented reality (AR)-powered advertising and promotional campaigns that used cutting-edge facial recognition technologies to increase user engagement and boost sales.

Later, Snapchat released a brand-new update that allowed users to digitize well-known landscapes, human body parts, and pets using immersive augmented reality objects. For example, the “Ground Transformation” AR filter now allows users to alter the appearance of floors and grounds by turning them into lava. Since the substitution effect can be customized, Forbes contends that it would become a game-changing and top-tier priority for industry-leading companies as they run multiple marketing campaigns by turning any terrain into tailored brand-on landscapes.

Snapchat's Face Recognition Technology, YT Trending - Latest Technology NewsSnapchat AR facial filters are currently more than just a novelty project; they are a fully-fledged digital economy and ecosystem for the majority of sector-specific businesses. To promote user engagement, lead generation, and branding improvements, they can make use of the platform’s augmented reality capabilities. For example, recently introduced an AR filter marketplace that enables brands and content producers to create unique AR effects and share them with the platform’s community on a paid or free basis.

Snapchat Facial Recognition: Success Timeline

When Snapchat purchased the Ukrainian computer vision startup Looksery in 2015, the company began to implement facial recognition technology. It achieved success after releasing an augmented reality (AR)-enabled virtual chat programme that introduced a new mechanism of digital communication. The $150 million investment was extremely worthwhile because it allowed the company to significantly improve its internal neural network engineering capabilities, which in turn allowed it to produce over 3,000 interesting AR facial filters for Snapchat users. This served as the foundation for the development of facial recognition technologies, which are now used in social media filters and masks.

A number of well-known companies, influencers, and celebrities joined the platform as the technology-driven release took off in the social media space. They were all interested in utilizing augmented reality tools to boost their individual brands’ visibility and sales endeavors.

As two early adopters of Snapchat’s facial recognition filters, Ariana Grande and Jessica Alba used 3D dog masks, bread faces, and golden goddess lenses to expand their fan bases and enhance their online visibility. More than that, Snapchat’s technologies were prominently displayed at the 2016 Oscars as well-known attendees began using the “Face Swap” filter to switch faces with Leonardo Di Caprio in support of the actor receiving a long-awaited award.

Since Snapchat’s technologically driven commercial success, computer vision-based facial recognition technology has advanced as well. Presently, ML-powered algorithms help brands in live streaming, healthcare, retail, e-commerce, manufacturing, and cosmetics to improve employee training, accelerate time to market (GTM), reduce product return rates, and improve the effectiveness of advertising campaigns.

How Snapchat’s facial tracking and recognition Function

The product has been using facial recognition and tracking algorithms since 2015 to identify human-based faces as particular points (1s or 0s) that relate to particular areas of face analysis saved in a database. They could be the lips, brows, ears, foreheads, and so on.

First, computer vision-based algorithms identify the points (1s or 0s) that correspond to the various facial features that are darker or lighter. In order to find frequently occurring points while scanning the camera image hash, neural networks process large amounts of coordinate data. Here comes the magic of augmented reality, allowing tech teams to tell faces apart from other object-specific objects in images.

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.

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