Offline Face Recognition & Liveness Detection in Flutter

Learn how to import the pre-built starter Flutter project for face detection and recognition. We'll walk through setting up the code and running it on Android and iOS simulators or real devices.

Understand the structure of the starter app, including basic UIimplementations. This sets the foundation for adding face detection and recognition features.

Integrate Google's ML Kit for face detection into your Flutter app. Learn how to configure Android permissions and dependencies to enable real-time face tracking.

Detect faces in real-time using ML Kit and display detailed results like bounding boxes and landmarks in the console. This helps verify the detection pipeline before UI integration.

Visualize detected faces by drawing rectangles over them using Flutter’s custom painter. Create a face detection overlay just like modern apps.

Build a custom FaceDetectorPainter class to handle drawing face bounding boxes and landmarks. This modular approach keeps your detection logic clean and reusable.

Learn to extract and crop individual faces from an image based on detected bounding boxes. Perfect for preparing data for face recognition or user registration.

Get started with face recognition by integrating TensorFlow Lite models in Flutter. Set up dependencies and prepare your app for running MobileFaceNet or FaceNet models.

Load the TensorFlow Lite model and extract face embeddings from detected faces. These embeddings are the foundation for accurate face matching and identification.

Save extracted face embeddings along with user names in a local database. This enables face recognition by comparing live input with stored profiles.

Compare live face embeddings with stored entries to recognize registered users. Implement a simple yet powerful face matching system right inside your Flutter app.

Enhance your face recognition app by showing the names of identified users directly on the screen

Fix common problems like false positives or missed matches in your face recognition app. Learn how to set and fine-tune the similarity threshold for accurate identification.

Run tests to detect and recognize multiple faces using your Flutter app. Ensure your app performs accurately with varied face positions and lighting conditions.

Integrate the FaceNet model in your Flutter app to generate high-quality face embeddings. Learn how FaceNet improves recognition accuracy over traditional methods.

Discover practical ways to enhance face matching accuracy using better lighting, image quality, and threshold tuning. Optimize performance for real-world face recognition scenarios.

Learn how to format and pass face images into the TensorFlow Lite model and retrieve the output embeddings. Understand input preprocessing and model output interpretation.

Understand how face embeddings and user names are saved in a structured database. Learn the logic behind mapping each face to a unique identity for accurate recognition.

Learn how to securely store registered faces by saving their unique embeddings and user info. This enables consistent and reliable face recognition during future logins.

Build a Flutter screen to display all registered users with their names and face data. Manage and visualize your face recognition database in an intuitive UI.