Added #

• LivenessView takes optional sessionKey and sessionId, used to sign the captured images.
• ExtractedLiveness.tsMs: the timestamp both images were signed with. Send it as backend_ts_ms.

Fixed #

• Android: Kotlin compile errors in LivenessView.kt.
• Android: apps that also use LiteRT no longer fail with "Duplicate class org.tensorflow.lite...". The plugin now depends on LiteRT 1.4.0 instead of org.tensorflow:tensorflow-lite-support:0.4.3.
• iOS: Swift compile errors in LivenessView.swift.
• iOS: no longer pins TensorFlowLiteSwift 2.10.0 or GoogleMLKit/FaceDetection, which conflicted with apps on TensorFlow Lite 2.12. Depends on GoogleToolboxForMac, which the SDK links at launch.
• iOS: only the precompiled liveness.metallib ships; the raw .metal sources caused "duplicated symbols" under use_frameworks!.

Changed #

• Updated native SDK binaries (Android AAR, iOS xcframework).

Added #

• Handle case mask pink

Added #

• Run android

Added #

• Changed sdk embedding to static

Added #

• Add EKYC Face Liveness detection
• Add EKYC Card detection

Added #

• Update SDK version
• With new feature

Added #

• Update SDK version
• With new feature

Added #

• Initial release of eKYC Plugin
• Native face liveness detection for Android and iOS
• LivenessView widget for easy integration
• Camera flash support with configurable isFlash parameter
• ExtractedLiveness data model with base64-encoded images
• Complete Android integration with Platform Views support
• Complete iOS integration with TensorFlow Lite
• Base64 image extraction for:
  • Original face image
  • Color-processed liveness image
  • SDK color information for anti-spoofing
• Comprehensive documentation with integration guides
• Example application demonstrating full implementation
• Support for Android API 23+ (Android 6.0+)
• Support for iOS 13.4+

Features #

• Face Liveness Detection: Real-time detection of live human faces vs. photos/videos
• Camera Integration: Native camera handling with flash control
• Platform Views: Seamless native UI integration in Flutter apps
• TensorFlow Lite: Powered by TensorFlow Lite for accurate face detection
• Cross-platform API: Unified API for both Android and iOS
• Anti-spoofing: Built-in color detection for enhanced security

Documentation #

• Complete README with installation and usage instructions
• Detailed Android integration guide with Gradle configuration
• Detailed iOS integration guide with Podfile setup
• Troubleshooting section for common issues
• Best practices for performance, UX, and security
• API reference for all public classes and methods
• Code examples for basic and advanced usage

Technical Details #

• Minimum Flutter SDK: 3.3.0
• Minimum Dart SDK: 3.3.2
• Android minSdkVersion: 23
• Android compileSdkVersion: 34
• iOS Deployment Target: 13.4
• Kotlin version: 1.9.25
• Swift version: 5.0

Known Limitations #

• Requires physical device for testing (camera functionality)
• bridge_native dependency currently uses local path (needs to be published separately for pub.flutter-io.cn)
• iOS Simulator support limited due to TensorFlow Lite architecture constraints

Added #

• Initial project structure
• Basic plugin scaffolding
• Placeholder for future implementation