What you’ll learn
- Build fully offline face recognition apps with no internet or paid APIs
- Develop face-based security and attendance apps in Flutter
- Understand how face detection and recognition work under the hood
- Implement real-time face recognition with liveness detection
- Capture live camera feed and process real-time frames in Flutter
- Register and manage multiple faces locally on the device
- Perform face recognition with FaceNet & MobileFaceNet (TFLite models)
- Detect faces in images using Google ML Kit in Flutter
Want to build powerful face detection and recognition apps in Flutter—without relying on paid APIs or internet connection? This hands-on course teaches you step-by-step how to integrate Face Detection and Face Recognition using TensorFlow Lite and Google ML Kit in Flutter for both image-based and real-time video recognition.
Whether you’re aiming to create a face recognition-based attendance app, a smart security system, or simply want to integrate AI facial features into your Flutter project, this course is your complete guide.
What You’ll Learn:
- Understand the basics and background of face recognition technology
- Set up Flutter development environment on Windows & macOS
- Build an Image Picker App to capture or select photos from the gallery
- Implement Face Detection using Google ML Kit
- Perform Face Recognition with FaceNet & MobileFaceNet models (TensorFlow Lite)
- Register and recognize faces from images
- Manage and match multiple face records
- Capture and process camera frames in real-time
- Perform real-time face recognition with liveness detection
- Register faces from multiple angles for improved accuracy
- Build fully offline face recognition apps—no need for paid APIs or internet
- Use the concepts to create attendance, authentication, and security systems in Flutter
Why Take This Course?
- Offline Capability – Build apps that work without internet using TensorFlow Lite
- Zero API Cost – No paid services required, everything runs on-device
- Privacy Focused – All data and recognition stay local
- Real-time Apps – Learn how to work with live camera feeds in Flutter
- Fully Practical – Project-based learning for real-world applications
Who This Course Is For:
- Flutter developers interested in integrating AI-powered facial features
- Mobile app developers building security or attendance systems
- Beginners and intermediates looking to explore Face Recognition in Flutter
- Anyone who wants to learn offline face recognition with no paid API usage
Technologies Covered:
- Flutter & Dart
- TensorFlow Lite (TFLite)
- Google ML Kit Face Detection
- FaceNet & MobileFaceNet Models
- Real-time Camera Integration
- Image Picker & Camera Plugins
By the end of this course, you will have the confidence and skills to build robust face recognition apps using Flutter—from image-based verification to real-time, camera-based detection and recognition, all without internet.
Enroll now and start building smart, offline AI-powered Flutter apps today!
Who this course is for:
- Anyone building security, authentication, or attendance systems in Flutter
- Developers looking to avoid paid APIs and build fully offline apps
- Beginners curious about face detection and recognition in real-world apps
- Tech enthusiasts eager to learn TensorFlow Lite with Flutter
- Students and hobbyists who want hands-on experience with ML in mobile apps
- Flutter developers who want to integrate AI features into their apps
- Mobile developers interested in offline face recognition solutions
