flutter_liveness 1.0.0

On-device face liveness detection for Flutter using a MobileNetV2 model. Detect spoof attacks from photos and videos, no internet required.

flutter_liveness

Lightweight on-device face liveness detection for Flutter, powered by MobileNetV2 from open liveness research.

✨ Features

• On-device liveness detection — no server required
• Ships with pre-trained MobileNetV2 model
• Uses TensorFlow Lite — optimized for real-time
• Simple API — plug & play
• Blur & clarity detector (Laplacian filter)

🧠 Model Information

This package uses a pre-trained MobileNetV2 face liveness model trained as described in:

Using MobileNetV2 (Zawar, et al., 2023)

From the open-source project: biometric-technologies/liveness-detection-model

Model licensed under MIT, included with attribution.

Class meaning:

• 0 = real
• 1 = spoof

🚀 Usage

Initialize

import 'package:flutter_liveness/flutter_liveness.dart';
import 'package:image/image.dart' as imglib;

late final FlutterLiveness liveness;

Future<void> initLiveness() async {
  liveness = await FlutterLiveness.create();
}

Analyze a face image

Future<void> check(Uint8List faceBytes) async {
  final img = imglib.decodeImage(faceBytes);
  if (img == null) throw Exception("Invalid image");

  final result = await liveness.analyze(img);

  print("Live: ${result.isLive}");
  print("Score (P(spoof)): ${result.score}");
  print("Laplacian: ${result.laplacian}");
  print("Inference time: ${result.time.inMilliseconds} ms");
}

Cleanup

@override
void dispose() {
  liveness.dispose();
  super.dispose();
}

📊 Output Example

Live: true
Score (P(spoof)): 0.023
Clarity (laplacian): 1812
Inference time: 29 ms

⚙️ Settings

You can tune thresholds:

final liveness = await FlutterLiveness.create(
  options: LivenessOptions(
    threshold: 0.5, // sigmoid cutoff
    laplacianThreshold: 1000, // blur block
  ),
);

❗ Usage Notes

• Feed cropped face images (not full camera frames)
• Calls are fast (~20–35ms on modern devices)
• Throttle analysis: ~3–5 FPS recommended
• Do not consider this as the only security mechanism for KYC/financial apps — use it as one signal in a trust pipeline

📱 Integration Tips

• Use camera plugin to stream frames
• Detect faces with MLKit or any detector
• Crop → send to liveness.analyze()
• Combine with:
  • Face presence
  • Eye blink / movement (optional future enhancement)
  • Anti-tampering / screen detection

🔐 Disclaimer

This library provides liveness estimation, not identity verification. Use responsibly and evaluate for your use-case security requirements.

🤝 Acknowledgements

• TensorFlow Lite team
• Biometric Technologies OÜ for the model & research
• Flutter community