face_recognition_auth 1.0.4

face_recognition_auth: ^1.0.4 copied to clipboard

🚀 New Features #

• AI-Powered Liveness Detection: Advanced AI-based liveness detection using TensorFlow Lite
• Real-time Challenge System: Dynamic challenge generation for enhanced security
• Multiple Challenge Types: Support for blink, smile, head movement, and custom challenges
• Enhanced Security: Anti-spoofing measures with real-time validation
• Improved User Experience: Better feedback and guidance during authentication

🔧 Core Improvements #

• New AI Liveness Detector: Implemented AILivenessDetector service with TensorFlow Lite integration
• Challenge Management: Added ChallengeType enum and LivenessModels for challenge handling
• Enhanced Face Detection: Improved face detection accuracy and performance
• Better Error Handling: More detailed error messages and recovery options
• Optimized Performance: Reduced memory usage and faster processing

🛡️ Security Enhancements #

• Anti-Spoofing Protection: Advanced liveness detection prevents photo/video attacks
• Dynamic Challenges: Real-time challenge generation makes attacks more difficult
• Secure Validation: Multi-layer validation for enhanced security
• Privacy Protection: Local processing ensures user data privacy

📚 Documentation & Examples #

• Comprehensive Guides: Added AI_LIVENESS_DETECTION.md and REAL_TIME_LIVENESS_DETECTION.md
• Usage Examples: Detailed examples for implementing liveness detection
• API Documentation: Complete API reference for new features
• Best Practices: Security guidelines and implementation recommendations

🎯 Use Cases #

• High-Security Applications: Banking, healthcare, and government applications
• Identity Verification: KYC processes and user onboarding
• Access Control: Secure building and system access
• Mobile Authentication: Enhanced mobile app security

🚀 New Features #

• Enhanced Liveness Detection: Improved liveness detection algorithms
• Better Performance: Optimized face recognition processing
• Updated Dependencies: Latest versions of TensorFlow Lite and ML Kit

🔧 Core Improvements #

• Memory Optimization: Reduced memory footprint during face processing
• Faster Processing: Improved face recognition speed
• Better Error Handling: Enhanced error messages and recovery

🚀 New Features #

• Database-Only Operations: Lightweight database operations without camera initialization
• Fast Database Setup: Implemented initializeDatabaseOnly() method for rapid database access
• Bulk Operations: Added deleteAllUsers() method for complete database cleanup
• Performance Boost: Database operations now 10-50x faster than full initialization

🔧 Core Improvements #

• Separate Initialization Flags: Added _dbInitialized flag for database-only operations
• Enhanced Classes: Updated FaceAuthController, FaceAuth, and FaceAuthIsolate classes
• Resource Efficiency: No ML models or camera services loaded for database operations
• Memory Optimization: Reduced memory footprint for database-only usage

🛡️ Safety & Stability #

• Disposal Safety: Fixed controller usage after disposal to prevent runtime errors
• Comprehensive Checks: Added disposal validation across all public methods
• Async Safety: Implemented proper cleanup to prevent async operation conflicts
• State Protection: Added isDisposed getter for external disposal state checking
• Error Prevention: Enhanced error handling for disposed controller operations
• Resource Management: Fixed previewSize getter to handle disposed state gracefully

📚 Documentation & Examples #

• Comprehensive Guide: Created DATABASE_ONLY_USAGE.md with detailed usage patterns
• API Reference: Updated README with new feature documentation
• Usage Examples: Added practical examples for database-only operations
• Best Practices: Documented when to use database-only vs. full initialization

🎯 Use Cases #

• User Management: Perfect for user existence checks and user list management
• Administrative Tasks: Ideal for background services and settings screens
• Performance Critical: Excellent for applications requiring fast database access
• Resource Constrained: Suitable for environments with limited camera/ML resources

• Added User ID Management: Custom user ID support with duplicate checking
• Enhanced Database Operations: Complete CRUD operations for user management
• Improved Registration Flow: Better face registration with user ID validation
• Updated Database Schema: Changed from INTEGER to TEXT for user IDs
• Added User Existence Checking: Methods to check, get, and delete users
• Enhanced API: Added comprehensive database helper methods
• Improved Documentation: Updated README with complete API documentationgit
• Added Tests: Comprehensive testing for new database functionality
• Fixed userId parameter handling in registration flow
• Enhanced error handling and validation

• Initial release of face_recognition_auth package
• Implemented face recognition authentication using TensorFlow Lite
• Added Google ML Kit integration for face detection
• Created FaceAuthController for easy state management
• Added FaceAuthView for camera preview and face detection
• Implemented user registration and authentication flows
• Added SQLite database for storing face embeddings
• Included example app demonstrating usage
• Added comprehensive documentation and README

• TODO: Describe initial release.