Day 3: Implementing Face Detection #FaceDetection #MobileAIApp

Face detection is the first step in building a deepfake face swap app. On Day 3, we’ll integrate a pre-trained model like MTCNN or OpenCV for face detection. Users will be able to upload or capture images, and the app will detect faces and highlight them.

Table of Contents

1. Overview of Face Detection

What Is Face Detection?

Face detection identifies and localizes faces in an image. It involves:

Detecting Face Regions: Using bounding boxes.
Mapping Facial Landmarks: Identifying key features like eyes, nose, and mouth.

Why Use Pre-Trained Models?

Saves development time.
Optimized for accuracy and performance on mobile devices.
Popular models include MTCNN, Dlib, and OpenCV’s Haar Cascades.

2. Installing Face Detection Libraries

Using TensorFlow.js

Install TensorFlow.js for running models:

npm install @tensorflow/tfjs @tensorflow-models/face-landmarks-detection

Using OpenCV (Optional Alternative)

Install OpenCV for mobile-optimized face detection:

npm install react-native-opencv

3. Integrating Image Picker for Uploads

Step 1: Modify the Home Screen

Allow users to pick images for detection:

import React from 'react';
import { View, Button, StyleSheet } from 'react-native';
import * as ImagePicker from 'expo-image-picker';

export default function HomeScreen({ navigation }) {
    const pickImage = async () => {
        const result = await ImagePicker.launchImageLibraryAsync({
            mediaTypes: ImagePicker.MediaTypeOptions.Images,
            allowsEditing: true,
            quality: 1,
        });

        if (!result.cancelled) {
            navigation.navigate('FaceSwap', { imageUri: result.uri });
        }
    };

    return (
        <View style={styles.container}>
            <Button title="Upload Image" onPress={pickImage} />
        </View>
    );
}

const styles = StyleSheet.create({
    container: { flex: 1, justifyContent: 'center', alignItems: 'center' },
});

4. Implementing Face Detection

Step 1: Initialize TensorFlow and Load the Model

In FaceSwapScreen.js, initialize TensorFlow.js and the face detection model:

import React, { useState, useEffect } from 'react';
import { View, Image, StyleSheet, ActivityIndicator } from 'react-native';
import * as tf from '@tensorflow/tfjs';
import * as faceLandmarksDetection from '@tensorflow-models/face-landmarks-detection';
import Canvas from 'react-native-canvas';

export default function FaceSwapScreen({ route }) {
    const { imageUri } = route.params;
    const [model, setModel] = useState(null);
    const [loading, setLoading] = useState(true);

    useEffect(() => {
        const loadModel = async () => {
            await tf.ready();
            const loadedModel = await faceLandmarksDetection.load(
                faceLandmarksDetection.SupportedPackages.mediapipeFacemesh
            );
            setModel(loadedModel);
            setLoading(false);
        };

        loadModel();
    }, []);

    return (
        <View style={styles.container}>
            {loading ? (
                <ActivityIndicator size="large" color="#0000ff" />
            ) : (
                <Image source={{ uri: imageUri }} style={styles.image} />
            )}
        </View>
    );
}

const styles = StyleSheet.create({
    container: { flex: 1, justifyContent: 'center', alignItems: 'center' },
    image: { width: 300, height: 400 },
});

Step 2: Detect Faces in the Uploaded Image

Update FaceSwapScreen.js to run face detection on the image:

useEffect(() => {
    const detectFaces = async () => {
        const image = document.createElement('img');
        image.src = imageUri;

        const predictions = await model.estimateFaces({ input: image });

        console.log('Face Predictions:', predictions); // Log detected faces
        // Add visualization logic here
    };

    if (model) {
        detectFaces();
    }
}, [model]);

Step 3: Highlight Detected Faces

Use a Canvas overlay to draw bounding boxes around detected faces:

const drawFaceBoundingBoxes = (canvas, predictions) => {
    const ctx = canvas.getContext('2d');
    ctx.strokeStyle = 'red';
    ctx.lineWidth = 2;

    predictions.forEach((prediction) => {
        const [x, y, width, height] = prediction.boundingBox;
        ctx.strokeRect(x, y, width, height);
    });
};

<Canvas
    ref={(canvas) => {
        if (canvas) {
            drawFaceBoundingBoxes(canvas, predictions);
        }
    }}
/>;

5. Testing the Face Detection

Step 1: Run the App

Start the app and upload an image:

expo start

Step 2: Verify Detection

Confirm the model detects faces in the uploaded image.
Ensure bounding boxes align correctly with facial features.

6. Next Steps

On Day 4, we’ll:

Extract and align facial features for face swapping.
Dive into landmark mapping to prepare faces for transformations.

References and Links

SEO Keywords: face detection app tutorial, TensorFlow.js face detection, React Native AI app, building deepfake apps, mobile face detection tutorial.

Post Views: 89