On Day 2, we’ll set up camera access and integrate real-time face tracking using Expo Camera and MediaPipe Face Mesh. By the end of today, our app will detect faces in live video and extract facial landmarks.
1. Enabling Camera Access in Expo
To process real-time facial tracking, we need access to the device camera.
Step 1: Install Expo Camera
expo install expo-camera
Step 2: Add Permissions to app.json
For Android:
"android": {
"permissions": ["CAMERA"]
}
For iOS:
"ios": {
"infoPlist": {
"NSCameraUsageDescription": "This app requires camera access for real-time face tracking."
}
}
2. Creating the Camera Component
Step 1: Create CameraScreen.js
Inside src/components/, create a new file:
import React, { useState, useEffect, useRef } from 'react';
import { View, StyleSheet, Text } from 'react-native';
import { Camera } from 'expo-camera';
export default function CameraScreen() {
const [hasPermission, setHasPermission] = useState(null);
const cameraRef = useRef(null);
useEffect(() => {
(async () => {
const { status } = await Camera.requestPermissionsAsync();
setHasPermission(status === 'granted');
})();
}, []);
if (hasPermission === null) {
return <View />;
}
if (hasPermission === false) {
return <Text>No access to camera</Text>;
}
return (
<View style={styles.container}>
<Camera ref={cameraRef} style={styles.camera} type={Camera.Constants.Type.front} />
</View>
);
}
const styles = StyleSheet.create({
container: { flex: 1 },
camera: { flex: 1 },
});
Step 2: Add Camera to App.js
Replace the contents of App.js with:
import React from 'react';
import { NavigationContainer } from '@react-navigation/native';
import { createStackNavigator } from '@react-navigation/stack';
import CameraScreen from './src/components/CameraScreen';
const Stack = createStackNavigator();
export default function App() {
return (
<NavigationContainer>
<Stack.Navigator>
<Stack.Screen name="Camera" component={CameraScreen} />
</Stack.Navigator>
</NavigationContainer>
);
}
3. Integrating MediaPipe Face Tracking
Step 1: Install TensorFlow.js and Face Mesh Model
npm install @tensorflow/tfjs @tensorflow-models/facemesh
Step 2: Load the Face Mesh Model in CameraScreen.js
Modify CameraScreen.js to initialize the model:
import * as tf from '@tensorflow/tfjs';
import * as faceLandmarksDetection from '@tensorflow-models/face-landmarks-detection';
const [model, setModel] = useState(null);
useEffect(() => {
const loadModel = async () => {
await tf.ready();
const loadedModel = await faceLandmarksDetection.load(
faceLandmarksDetection.SupportedPackages.mediapipeFacemesh
);
setModel(loadedModel);
};
loadModel();
}, []);
Step 3: Process Camera Frames for Face Tracking
Add a function to detect faces in real time:
const detectFace = async (image) => {
if (!model) return;
const predictions = await model.estimateFaces({
input: image,
returnTensors: false,
flipHorizontal: false,
});
if (predictions.length > 0) {
console.log('Detected Face Landmarks:', predictions[0].scaledMesh);
}
};
Step 4: Capture Frames & Detect Faces
Modify CameraScreen.js to capture frames and process face detection:
import { useFrameProcessor } from 'react-native-vision-camera';
const frameProcessor = useFrameProcessor((frame) => {
detectFace(frame);
}, []);
4. Testing the Face Tracking
Step 1: Start the Development Server
expo start
Step 2: Verify Face Detection
- Open the app and grant camera permissions.
- Face the camera and check the console for landmark coordinates.
- Try different angles and lighting conditions.
5. Optimizing Face Tracking Performance
- Skip every alternate frame for real-time performance:
if (frameCount % 2 === 0) {
detectFace(frame);
}
- Reduce resolution before processing:
const resizeFrame = (frame, width, height) => {
return frame.resizeBilinear([width, height]);
};
- Use GPU Acceleration by enabling WebGL:
tf.setBackend('webgl');
6. Key Concepts Covered
✅ Enabled live camera access.
✅ Integrated MediaPipe Face Mesh for real-time face tracking.
✅ Optimized frame processing for better performance.
7. Next Steps: Mapping Facial Features to a 3D Avatar
Tomorrow, we’ll: 🔹 Extract key facial landmarks (eyes, mouth, nose, jaw).
🔹 Convert face data into 3D avatar movements using Three.js.
8. References & Learning Resources
9. SEO Keywords:
React Native face tracking, real-time AI avatars, TensorFlow.js face detection, building a VTuber app, mobile face landmark detection.
