Face Recognition Kit

Face Recognition Kit is a high-performance, professional-grade Flutter SDK designed for real-time facial detection and precision biometric identification. Built with a Crop-First Alignment Engine, it provides industry-leading accuracy for attendance systems, secure kiosks, and proctoring applications.

📊 Compatibility Matrix

Platform	Support	Min. OS Version	Hardware/Feature Requirements
Android	✅	API 21+ (Lollipop)	Camera support, OpenGL ES 3.0+
iOS	✅	iOS 12.0+	ARM64 Architecture (Physical device recommended)
Web	✅	Modern Browsers	WASM support, `SharedArrayBuffer` (for MediaPipe)
Flutter	✅	^3.35.0	Stable channel recommended

🚀 Key Professional Features

192D Biometric Distillation: High-dimensional vector extraction for lighting-invariant matching.
Precision Alignment Engine: Intelligent "Crop-then-Rotate" pipeline that eliminates coordinate drift.
Real-Time Enrollment Guidance: UX-optimized overlays guiding users to "Look Straight" or "Move Closer".
Neural Engine Diagnostics: Real-time detection of missing AI models with actionable developer warnings.
Unified Multi-Platform API: Consistent behavior across Android, iOS, and Web.
Production-Ready Components: Pre-built FaceScannerView and RecognitionDialog for rapid deployment.

🏗️ Technical Architecture

The SDK uses a multi-stage pipeline to ensure biometric integrity:

graph TD
    A[Camera Stream] --> B[ML Kit / MediaPipe Detection]
    B --> C{Quality Gate}
    C -- "Centered? Eyes Open?" --> D[Precision Crop Engine]
    C -- "Fail" --> E[Real-Time Guidance UI]
    D --> F[192D Neural Distillation]
    F --> G[Euclidean Distance Matching]
    G --> H[Identity Verified]

📦 Installation & Setup

1. Add Dependency

dependencies:
  face_recognition_kit: ^1.2.0

2. Biometric Model Setup (CRITICAL)

The SDK requires a mobile_facenet.tflite model.

Create assets/models/ in your project root.
Place your TFLite model file there.
Update pubspec.yaml:

flutter:
  assets:
    - assets/models/mobile_facenet.tflite

🛠️ Implementation Guide

Basic Scanner Implementation

Deploy a full-featured biometric scanner in minutes.

FaceScannerView(
  detector: FaceDetectorInterface(),
  recognizer: FaceRecognizerInterface(),
  profiles: myRegistry, // List<FaceProfile>
  enableDefaultDialog: true,
  onFaceRecognized: (profile, image) {
    print("Welcome back, ${profile.name}");
  },
)

Advanced UI Customization

Tailor the recognition experience to your brand.

FaceScannerView(
  // ... engines ...
  dialogOptions: RecognitionDialogOptions(
    primaryColor: Colors.indigo,
    successTitle: "Access Granted",
    welcomeMessage: "Hello, {name}!",
    displayDuration: Duration(seconds: 3),
  ),
  guidanceOptions: FaceGuidanceOptions(
    enabled: true,
    stayStillMessage: "Analysing biometrics...",
    moveCloserMessage: "Please center your face",
    // Custom UI Overlay Builder
    guidanceBuilder: (context, message) => MyCustomOverlay(text: message),
  ),
)

⚙️ API Reference

`FaceScannerView`

Property	Type	Description
`detector`	`FaceDetectorInterface`	The engine responsible for localizing faces.
`recognizer`	`FaceRecognizerInterface`	The engine responsible for biometric extraction.
`profiles`	`List<FaceProfile>`	Known identities to match against.
`captureOnlyFace`	`bool`	If true, returns only the cropped face image in callbacks.
`onFaceDetected`	`Function(FaceData, Uint8List)`	Triggered on every frame a face meets quality standards.

`FaceGuidanceOptions`

Property	Default	Description
`rotationThreshold`	`15.0`	Maximum allowed head tilt (degrees) for capture.
`eyeOpenThreshold`	`0.4`	Minimum probability for eyes to be considered open.
`brightnessThreshold`	`40.0`	Minimum average frame brightness required.

🏗️ Full Implementation Example

This example shows how to integrate the SDK with a standard identity provider and state management system.

import 'package:face_recognition_kit/face_recognition_kit.dart';

class BiometricAuthPage extends StatelessWidget {
  @override
  Widget build(BuildContext context) {
    return FaceScannerView(
      detector: FaceDetectorInterface(),
      recognizer: FaceRecognizerInterface(),
      profiles: userStore.allProfiles, // List<FaceProfile> from your backend
      enableDefaultDialog: true,
      captureOnlyFace: true,
      // Customising the UI guidance
      guidanceOptions: FaceGuidanceOptions(
        enabled: true,
        moveCloserMessage: "Align face for verification",
      ),
      // Triggered on successful match
      onFaceRecognized: (profile, image) {
        handleLoginSuccess(profile);
      },
      // Triggered on every valid face detection (useful for enrollment)
      onFaceDetected: (face, image) {
        if (isEnrollmentMode) saveBiometric(face, image);
      },
    );
  }
}

💡 Best Practices for 99.9% Accuracy

Lighting: Ensure even lighting on the face. Avoid strong backlighting (windows behind the user).
Distance: Users should hold the device at a natural arm's length (Face filling ~30% of the frame).
Registry Hygiene: Ensure the registered biometric profile is captured in good lighting with an upright head.
Mirroring: Use FaceScannerView defaults—it automatically handles front-camera mirroring correction.

✅ Production Checklist

Before publishing your application, ensure the following are met:

Model Asset: mobile_facenet.tflite is placed in assets/models/.
Release Mode: Test the engine in flutter run --release for optimal neural performance.
Android Min SDK: Ensure minSdkVersion 21 is set in build.gradle.
iOS Privacy Keys: NSCameraUsageDescription added to Info.plist.
Registry Prep: Train users to register in stable, front-lit environments for 99.9% consistency.

🌍 Real-Life Use Cases

Attendance Systems: Contactless clock-in/out for modern offices and classrooms.
Biometric Security: Adding identity verification to sensitive app features or financial transactions.
AI Proctoring: Continuous verification for secure remote examinations.
Personalized Kiosks: Recognizing and greeting customers at smart retail terminals.

📄 License

MIT Licensed. Developed for high-security biometric ecosystems.