Day 4: Implementing Face Recognition or Emotion Detection

Welcome to Day 4! Today, you’ll implement advanced machine learning capabilities like face recognition or emotion detection. These features can be used in security applications, social apps, or any context requiring user engagement analysis.

Table of Contents

What You’ll Learn Today

Set up a pre-trained model for face recognition or emotion detection.
Implement face detection using TensorFlow.js.
Use Core ML for face recognition on iOS.
Test the app with sample images.

Step 1: Download a Pre-Trained Face or Emotion Detection Model

TensorFlow.js: Use BlazeFace for face detection.
Core ML: Use Apple’s Vision framework, which provides face detection and emotion classification.

Step 2: Using TensorFlow.js for Face Detection

1. Install the BlazeFace Model

npm install @tensorflow-models/blazeface @tensorflow/tfjs-react-native

2. Set Up the Face Detection Model

Create a new file FaceDetector.js:

import * as tf from '@tensorflow/tfjs';
import '@tensorflow/tfjs-react-native';
import * as blazeface from '@tensorflow-models/blazeface';
import { decodeJpeg } from '@tensorflow/tfjs-react-native';

let model;

export const loadModel = async () => {
  await tf.ready();
  model = await blazeface.load();
  console.log('BlazeFace model loaded!');
};

export const detectFaces = async (imageData) => {
  if (!model) {
    console.error('Model not loaded.');
    return [];
  }
  const imageTensor = decodeJpeg(imageData);
  const predictions = await model.estimateFaces(imageTensor, false);
  return predictions;
};

3. Integrate Face Detection in the App

Update App.js:

import React, { useEffect, useState } from 'react';
import { View, Text, StyleSheet, Button, Image } from 'react-native';
import { loadModel, detectFaces } from './FaceDetector';
import * as ImagePicker from 'react-native-image-picker';

const App = () => {
  const [imageUri, setImageUri] = useState(null);
  const [faces, setFaces] = useState([]);

  useEffect(() => {
    loadModel();
  }, []);

  const pickImage = () => {
    ImagePicker.launchImageLibrary({}, (response) => {
      if (response.assets) {
        const uri = response.assets[0].uri;
        const base64 = response.assets[0].base64;
        setImageUri(uri);
        runFaceDetection(base64);
      }
    });
  };

  const runFaceDetection = async (imageData) => {
    const predictions = await detectFaces(imageData);
    setFaces(predictions);
  };

  return (
    <View style={styles.container}>
      <Text style={styles.title}>Face Detection</Text>
      <Button title="Pick an Image" onPress={pickImage} />
      {imageUri && <Image source={{ uri: imageUri }} style={styles.image} />}
      {faces.map((face, index) => (
        <Text key={index} style={styles.face}>
          Face {index + 1}: Bounding Box - {JSON.stringify(face.topLeft)} to {JSON.stringify(face.bottomRight)}
        </Text>
      ))}
    </View>
  );
};

const styles = StyleSheet.create({
  container: {
    flex: 1,
    justifyContent: 'center',
    alignItems: 'center',
    padding: 20,
  },
  title: {
    fontSize: 24,
    fontWeight: 'bold',
    marginBottom: 20,
  },
  image: {
    width: 300,
    height: 400,
    marginVertical: 20,
  },
  face: {
    fontSize: 16,
    marginVertical: 5,
  },
});

export default App;

4. Run the App

npx react-native run-android
npx react-native run-ios

Pick an image and view detected faces along with their bounding boxes.

Step 3: Using Core ML for Face Detection

1. Use Vision Framework for Face Detection

Open ViewController.swift:

import UIKit
import Vision

class ViewController: UIViewController, UIImagePickerControllerDelegate, UINavigationControllerDelegate {
    override func viewDidLoad() {
        super.viewDidLoad()
    }

    func detectFaces(_ image: UIImage) {
        let request = VNDetectFaceRectanglesRequest { (request, error) in
            guard let results = request.results as? [VNFaceObservation] else { return }
            for face in results {
                print("Face detected at bounding box: \(face.boundingBox)")
            }
        }

        guard let cgImage = image.cgImage else { return }
        let handler = VNImageRequestHandler(cgImage: cgImage, options: [:])
        try? handler.perform([request])
    }

    @IBAction func pickImage(_ sender: UIButton) {
        let picker = UIImagePickerController()
        picker.delegate = self
        picker.sourceType = .photoLibrary
        present(picker, animated: true, completion: nil)
    }

    func imagePickerController(_ picker: UIImagePickerController, didFinishPickingMediaWithInfo info: [UIImagePickerController.InfoKey : Any]) {
        if let image = info[.originalImage] as? UIImage {
            detectFaces(image)
        }
        dismiss(animated: true, completion: nil)
    }
}

2. Run the App

Select an image from the gallery.
View detected face bounding boxes in the Xcode console.

Step 4: Bonus – Emotion Detection

Extend the face detection model by using a pre-trained emotion classification model for TensorFlow.js or Core ML.
For example, use FER+ (Facial Expression Recognition Plus) for emotion detection.

SEO Optimization for This Tutorial

Keywords: TensorFlow.js face detection, Core ML face recognition, React Native emotion detection, BlazeFace TensorFlow.js, Core ML Vision face detection.

Meta Description: Learn how to integrate face recognition and emotion detection into your mobile app using TensorFlow.js and Core ML. Full step-by-step tutorial with code samples.

Summary

Today, you implemented face recognition and emotion detection in your app. These capabilities open up possibilities for advanced user interaction and analysis.

What’s Next: Tomorrow, you’ll learn to handle real-time image data and integrate it with the device camera for on-the-fly processing.

Stay tuned for Day 5: Handling Image Data and Integrating with the Device Camera.

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