face_recognition_kit 1.2.0

A professional Face Recognition Kit with real-time detection, classroom-optimized recognition, and a role-based analytics dashboard.

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.

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📊 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

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🚀 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.

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🏗️ 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]

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📦 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.

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

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

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🛠️ 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),
  ),
)

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⚙️ 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.

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🏗️ 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);
      },
    );
  }
}

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💡 Best Practices for 99.9% Accuracy

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

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✅ 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.

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🌍 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.

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📄 License

MIT Licensed. Developed for high-security biometric ecosystems.