A sophisticated cross-platform facial recognition system for modern attendance tracking and employee management
FaceMap leverages advanced machine learning with Flutter to provide real-time facial authentication across all major platforms. Built for reliability in diverse enterprise environments with or without network connectivity.
| Capability | Implementation |
|---|---|
| Facial Detection | Google ML Kit integration with real-time processing |
| Recognition Engine | MobileFaceNet architecture via TensorFlow Lite |
| Authentication Flow | Branch-specific employee verification system |
| Offline Operation | Complete functionality without internet via Hive DB |
| Cross-Platform | Single codebase deployment across all major platforms |
- High-Precision Face Detection - ML Kit powered detection optimized for varying lighting conditions
- Secure Biometric Recognition - Fast and accurate face recognition using MobileFaceNet (99.4% accuracy)
- Enterprise-Grade Authentication - Branch-specific employee verification with role-based access control
- Comprehensive Attendance Management - Automated time tracking with custom report generation
- Offline-First Architecture - Full functionality in disconnected environments with Hive persistence
- Cross-Platform Consistency - Identical experience across mobile, desktop, and web platforms
- Minimal Resource Footprint - Optimized for performance even on lower-end devices
facemap/
├── assets/ # Assets and ML models
│ └── mobile_facenet.tflite # Pre-trained facial recognition model
├── lib/
│ ├── app/ # Application configuration
│ │ ├── config.dart # Environment configuration
│ │ ├── routes.dart # Navigation routes
│ │ └── theme.dart # UI theme definitions
│ ├── models/ # Data structures
│ │ ├── employee.dart # Employee model with Hive integration
│ │ ├── face_data.dart # Facial embedding storage
│ │ └── attendance.dart # Attendance record model
│ ├── screens/ # UI components
│ │ ├── auth/ # Authentication flows
│ │ ├── home/ # Main dashboard
│ │ ├── registration/ # Employee registration
│ │ └── verification/ # Face verification
│ ├── services/ # Core business logic
│ │ ├── ml/ # Machine learning services
│ │ │ ├── detector.dart # Face detection implementation
│ │ │ └── recognizer.dart # Face recognition implementation
│ │ ├── storage/ # Data persistence
│ │ │ ├── hive_service.dart # Local database implementation
│ │ │ └── sync_service.dart # Optional cloud synchronization
│ │ └── auth_service.dart # Authentication service
│ └── main.dart # Application entry point
├── platform/ # Platform-specific implementations
│ ├── android/ # Android configuration
│ ├── ios/ # iOS configuration
│ ├── web/ # Web configuration
│ └── desktop/ # Desktop platforms configuration
├── test/ # Automated testing
│ ├── unit/ # Unit tests
│ ├── widget/ # Widget tests
│ └── integration/ # Integration tests
└── pubspec.yaml # Project dependencies
| Category | Technologies |
|---|---|
| Framework | Flutter SDK |
| ML & Vision | Google ML Kit, TensorFlow Lite |
| State Management | Provider |
| Local Storage | Hive NoSQL Database |
| Image Processing | camera, image_picker |
| Utilities | intl, path_provider, ffi |
| Development | flutter_lints, build_runner |
- Flutter SDK 3.10+ (with Dart 3.0+)
- Android Studio / Xcode for native development
- Git
# Clone repository
git clone https://github.com/your-username/facemap.git
# Navigate to project directory
cd facemap
# Install dependencies
flutter pub get
# Generate Hive adapters (if needed)
flutter pub run build_runner build --delete-conflicting-outputs
# Run the application
flutter run-
ML Model Setup:
- The MobileFaceNet model is included in the assets directory
- No additional configuration required for basic usage
-
Branch Configuration:
- Edit
lib/app/config.dartto configure branch IDs and admin credentials
- Edit
flutter build apk --releaseflutter build ios --releaseflutter build web --releaseflutter build windows --release
flutter build macos --release
flutter build linux --release-
Administrator Setup:
- First-time login creates admin account
- Configure branch settings and access permissions
-
Employee Registration:
- Enter employee details
- Capture facial data from multiple angles for improved accuracy
- Assign to specific branch with appropriate permissions
-
Attendance Tracking:
- Employees verify identity through facial recognition
- System automatically records check-in/check-out times
- Data persists locally and can sync when connectivity is available
-
Reporting:
- Generate attendance reports by date range, department, or individual
- Export data in multiple formats (CSV, PDF)
Contributions are welcome! Please follow these steps:
- Fork the repository
- Create a feature branch:
git checkout -b feature-name - Commit changes:
git commit -m 'Add some feature' - Push to branch:
git push origin feature-name - Submit a pull request
Please ensure your code adheres to the project's style guide and passes all tests.
Developer: Abhishek Emmanual Hansdak
Email: abhishekhansdak53@gmail.com