Built a custom face analysis system for SQIN using TensorFlow Lite and Unity. The system does accurate facial feature recognition across devices without requiring ARKit or ARCore.
I developed a Unity plugin that integrates TensorFlow Lite for real-time 3D face mesh extraction. It’s based on Google’s MediaPipe Face Mesh technology but optimized for mobile performance. The plugin works consistently across iOS, Android, and even the Unity Editor, with no dependency on platform-specific AR frameworks. We’re getting 30+ FPS on mid-range devices with 468 3D facial landmark points.
The implementation involved building a custom C++ to C# bridge for TensorFlow Lite integration and careful memory management to stay within mobile constraints. The face scanner analyzes facial features to provide personalized skincare and beauty product recommendations tailored to face shape, skin tone, and other features.
The main challenge was achieving reliable face mesh extraction without platform-specific AR frameworks. This made the feature accessible to a much broader range of devices while maintaining high performance and accuracy.
Built with Unity, C#, C++, and TensorFlow Lite.