How To Add Face Tracking In AR Apps

Face tracking in AR allows developers to build immersive, personalized user experiences by mapping facial movements and applying real-time effects. Whether you’re developing filters, avatars, or health diagnostics, this tutorial provides a step-by-step guide to adding face tracking in AR apps using popular frameworks like ARKit, ARCore, WebAR, and third-party tools. Covering hardware requirements, key SDKs, and code implementation, this 2025 tutorial empowers developers to build accurate, efficient face-tracked AR applications for mobile and web.

Table of Contents

Why Face Tracking in AR Matters Today

Face tracking has emerged as one of the most engaging and commercially successful implementations of augmented reality. From Snapchat filters and Instagram lenses to health diagnostics and education apps, face tracking enables applications to map facial landmarks, expressions, and gestures in real-time.

In 2025, face tracking AR is not only more accurate and resource-efficient but also more accessible thanks to hardware advances and SDK improvements. This tutorial walks you through the complete process—from selecting the right SDK to implementing a functional face tracking experience.

Part 1: Understanding Face Tracking in AR

What Is Face Tracking?

Face tracking is the real-time detection and analysis of a user’s face in an AR environment, including:

Facial feature detection (eyes, nose, mouth)
Expression recognition (smile, blink, frown)
3D head pose estimation
Face mesh mapping for effects and overlays

Common Use Cases

Social filters (Snapchat, TikTok, Instagram)
Virtual try-on (makeup, eyewear, accessories)
Avatars and facial animation in games
Emotion analysis and accessibility tools

Part 2: Choosing the Right Face Tracking SDK

1. ARKit (iOS)

Face Tracking Support: Yes (iPhone X and above with TrueDepth camera)
Features: High-quality 3D face mesh, blend shapes, expression capture
Best For: iOS-exclusive apps with high precision

2. ARCore (Android)

Face Tracking Support: Yes (via Augmented Faces API)
Features: 468 facial landmark points, 3D mask and region attachments
Best For: Android devices with front-facing camera support

3. WebAR (e.g., 8thWall, Banuba, DeepAR)

Face Tracking Support: Yes (browser-based)
Features: Lightweight models, AR filters, mobile browser compatibility
Best For: Cross-platform AR experiences with no app download

4. OpenCV + MediaPipe

Face Tracking Support: Yes (custom build)
Features: Python or C++ implementation, real-time facial landmark detection
Best For: Custom apps and research projects

Also Read: 10 Best Free Tools for Building AR Apps: 2025 Guide for Developers & Creators

Part 3: Hardware Requirements

Mobile Devices

iOS: iPhone X or newer with TrueDepth
Android: Camera2 API-supported phones (e.g., Pixel series, Samsung S series)

Desktops & Web

HD webcam required
GPU acceleration recommended (WebGL/WebGPU)

Part 4: Setting Up Your Development Environment

For iOS (ARKit)

Xcode 15+
iOS 16+
Swift 5
Apple developer account
TrueDepth-enabled device

For Android (ARCore)

Android Studio
ARCore SDK
Android 10+
Compatible Android phone

For WebAR

8thWall/WebAR SDK subscription or free tier
Web server setup with HTTPS
Browser with camera access permissions

Also Read: 10 Best WebAR Tools That Don’t Need App Downloads

Part 5: Building a Face Tracking App (Step-by-Step)

1. Create a Basic App Skeleton

iOS (ARKit) Example:

import ARKit
import SceneKit

class ViewController: UIViewController, ARSCNViewDelegate {
    @IBOutlet var sceneView: ARSCNView!

    override func viewDidLoad() {
        super.viewDidLoad()
        guard ARFaceTrackingConfiguration.isSupported else {
            fatalError("Face tracking is not supported on this device.")
        }
        sceneView.delegate = self
        sceneView.session.run(ARFaceTrackingConfiguration())
    }
}

2. Add a Face Mesh Overlay

func renderer(_ renderer: SCNSceneRenderer, nodeFor anchor: ARAnchor) -> SCNNode? {
    guard let faceAnchor = anchor as? ARFaceAnchor else { return nil }
    let geometry = ARSCNFaceGeometry(device: sceneView.device!)!
    geometry.update(from: faceAnchor.geometry)
    return SCNNode(geometry: geometry)
}

3. Attach a 3D Object or Effect

To add glasses, a mask, or effects:

let node = SCNNode()
let glasses = SCNScene(named: "art.scnassets/glasses.scn")!
node.addChildNode(glasses.rootNode)

4. WebAR Example (8thWall HTML/JS)

<a-scene xrextras-runtime xrextras-face-anchors>
  <a-entity face-attachment>
    <a-entity geometry="primitive: plane; height: 0.1; width: 0.2"
              material="shader: flat; src: #yourTexture"></a-entity>
  </a-entity>
</a-scene>

Part 6: Performance Optimization Tips

Use GPU-accelerated frameworks (Metal for iOS, Vulkan for Android)
Limit the number of face features tracked in lightweight use cases
Optimize 3D models for mobile (reduce poly count, textures)
Offload facial recognition to AI chips if available (Apple Neural Engine, Qualcomm Hexagon)

Also Read: Creating WebAR with AR.js: Step-by-Step AR.js Tutorial

Part 7: Testing and Debugging

Testing Tools

iOS Simulator (limited for ARKit Face)
Android Emulator (not suitable for ARCore face tracking)
On-device testing is strongly recommended for both

Common Issues

Poor lighting can reduce accuracy
False detections on low-end cameras
Frame drops if 3D assets aren’t optimized

Part 8: Publishing and Privacy Considerations

App Store and Play Store Policies

Clearly declare use of camera and face data
Include privacy policies that explain data usage
Offer opt-in consent for any facial recognition beyond filters

Security

Do not store or transmit face data without encryption
Follow GDPR, CCPA, and platform-specific privacy regulations

Conclusion: Future of Face Tracking in AR

By integrating face tracking in AR, developers create applications that connect more intuitively with users. As hardware continues to evolve and AI models grow smarter, the depth and realism of face tracking will increase, unlocking new possibilities in fashion, education, health, and entertainment.

In 2025, even indie developers can build face tracking apps with powerful features thanks to SDKs like ARKit, ARCore, and WebAR libraries. Whether for fun or function, face-tracked AR is a must-have feature in today’s immersive ecosystem.

Also Read: The Role of Time-Perception Algorithms in Future AR Glasses for ADHD

FAQs About Face Tracking in AR (2025)

Can face tracking be done offline?
Yes. SDKs like ARKit and MediaPipe allow offline facial landmark tracking with no data sent to cloud.
How accurate is facial expression detection in 2025?
Most SDKs can detect over 50+ expressions with over 90% accuracy using AI-enhanced models.
What’s the difference between face detection and face tracking?
Detection identifies a face in the frame; tracking follows it and maps landmarks in real-time.
Are there face tracking libraries for Unity developers?
Yes. Unity AR Foundation supports face tracking via ARKit/ARCore integrations.
Can face tracking work with multiple faces simultaneously?
ARKit supports up to one face at a time; ARCore and WebAR may allow multiple depending on the framework.
Is face tracking AR suitable for healthcare apps?
Yes. It’s used for facial therapy, emotion tracking, and contactless health diagnostics.
What’s the latest advancement in WebAR face tracking in 2025?
Lightweight AI models running on WebAssembly allow real-time tracking without native apps.
How to test face tracking on desktop without mobile?
Use webcam-based demos in 8thWall or run OpenCV/MediaPipe with Python on your laptop.
Are there any open-source face tracking datasets?
Yes. Datasets like 300-W, AFLW2000, and FaceWarehouse are popular for training custom models.
What are the top SDKs in 2025 for face tracking AR?
ARKit (iOS), ARCore (Android), 8thWall (Web), Banuba (Web/Mobile), and DeepAR lead the pack.

How to Add Face Tracking in AR Apps