Facial tracking technology utilises computer vision technologies to match moving objects on images or videos to apply specific AR-powered effects or handle processing operations. The technology went viral in the 2010s when multiple world-known brands like Instagram and Snapchat started empowering their solutions with augmented reality capabilities like filters and real-time 3D masks.
However, many companies still doubt whether facial tracking is a viable and investment-worthy technology to implement in their self-developed apps or internal products for in-house staff training, for example.
So, this post will guide you through the technical and business sides of facial tracking, covering the step-by-step algorithmic operations and up-to-date use cases of adopting the technology within real-life examples.
Face Tracking: How It Works Technically
Face tracking is about locating human faces in images and video content. The computer vision-based algorithms utilize computer-produced filters to convert facial image content into numerical expressions for further comparison and similarity analysis. The so-called filters get generated by using deep learning algorithms designed to process large volumes of data.
The core technical challenges of face tracking include: in-time face variability, pose variations, occlusions and illumination of the tracked object. These core issues decrease the software accuracy and performance level as algorithms simply lose the tracked objects while interpolating predictable object points.
Here come three well-known approaches to face tracking that help solve such issues:
- Low-level feature
- Template matching
- Statistical inference.
The low-level feature method utilizes low-level face knowledge (skin colour, background noise, etc.) or motion data to track facial objects.
The template matching approach involves tracking contours with snakes, 3D face model matching, and wavelet network matching.
The statistical inference approach utilizes Kalman filtering techniques for uni-modal Gaussian representations, Monte Carlo techniques for non-Gaussian non-linear target tracking, and Bayesian Network inference approaches.
All these approaches were designed to process camera image data and build precise face tracking sequences for real-time accurate matching of virtual 3D objects to human faces.
Business-Centric Use Cases of Adopting Face Tracking
As technology evolves, computer vision-based algorithms migrate from old-fashioned industrial needs to consumer and commercial domains. It’s now required to solve up-to-date and on-demand challenges like power consumption, real-time performance, stability, and memory requirements fit.
We want to discuss the core use cases and adoption scenarios of how face tracking technologies are currently utilized within core business domains.
- Face Filtering
Multiple market-specific brands can boost user engagement with Snapchat-like AR-enabled real-time filters, lenses, and 3D masks.
- Product Try-on
E-commerce, retail, beauty, and cosmetics companies can empower their business apps with top-notch virtual try-on capabilities to reduce product return rates and increase user-generated content for streamlined organic content promotion.
- AR beauty
Beauty-related market players can adopt AR SDK software to let users try on their desired outfits in real-time.
- Photo/video editing
YouTube-like platforms, live streaming, e-learning, product marketing, and other companies can integrate the AR-powered software development kit to streamline the video content creation process by adding face augmented reality effects, must-have pre-made video editing modules, and stunning filters.
- Games
Live content broadcasters, video game review brands, and gaming content creators can transform their content production process into next-gen experiences with face-controlled avatars, tailor-made animated backgrounds, and other AR assets with real-time overlay features.
Face Tracking Technology Example: Banuba AR SDK
Banuba’s AI-driven face tracking technology is an excellent example of utilizing computer vision capabilities to provide cutting-edge virtual experiences for end-users by empowering companies’ products. The vendor’s algorithm accurately spots and tracks up to several faces on a video in real-time. This enables brands to provide end-users with real-life 3D object overlay functionality, AR facial filters, avatar, and emoji support.
Banuba offers full-featured mobile device support and high performance with over 70% occlusion even at low lighting. More than that, the face tracking technology is built upon the 3D math model.
It’s an advanced algorithm that tracks human faces by marking up to 36 metrics as face morphs in real-time. They include facial expressions, in-video face positioning, anthropometry, and others. This facilitates mobile device optimization as the algorithm accurately tracks camera content.
This non-landmark face tracking approach helped AR technology Banuba to achieve:
– Enhanced performance. Non-landmark tracking leads to utilizing up to 36 metrics as face morphs, resulting in higher SDK performance and streamlined accuracy.
– Anti-jitter technologies. Non-landmark resource-efficient technology allowed the vendor’s tech teams to allocate resources and patent anti-jitter technology which minimizes potential real-time content jitters and ensures stable SDK performance for mobile, web, and desktop experiences.
– Multi-time algorithmic framing. Tech teams run algorithmic operations several times for each frame which boosts accuracy and shows the precise constant data (human face) and the variable one (background noise). This way, Banuba AR SDK provides a lag-free experience by detecting and handling multiple noises within each frame.
Also, the AR-powered tech solution provider serves multiple market-specific business domains like e-commerce, retail, social media, beauty, cosmetics, live streaming, gaming, e-learning, and others.
So, the core functionalities of Banuba’s face tracking technology include:
- Up to 3,308 vertices for 3D modelling of facial features
- Multi-face tracker
- <7-meter human-to-camera distance support
- -90*-to-90* face tracking angle support
- Poorly-lit environment support
- 70% facial occlusion for noise
- High-performing and stable facial detection with facial accessories
- 360-degree camera rotation support.
