Face Detection & Recognition In Flutter - The 2025 Guide

Master Face Recognition & Detection in Flutter Apps with TensorFlow Lite Models -No Internet, No Paid API, Fully Offline

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

Requirements

A computer (Windows or macOS) capable of running Flutter

A willingness to learn

Description

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 technologySet up Flutter development environment on Windows & macOSBuild an Image Picker App to capture or select photos from the galleryImplement Face Detection using Google ML KitPerform Face Recognition with FaceNet & MobileFaceNet models (TensorFlow Lite)Register and recognize faces from imagesManage and match multiple face recordsCapture and process camera frames in real-timePerform real-time face recognition with liveness detectionRegister faces from multiple angles for improved accuracyBuild fully offline face recognition apps—no need for paid APIs or internetUse the concepts to create attendance, authentication, and security systems in FlutterWhy Take This Course? Offline Capability – Build apps that work without internet using TensorFlow LiteZero API Cost – No paid services required, everything runs on-devicePrivacy Focused – All data and recognition stay localReal-time Apps – Learn how to work with live camera feeds in FlutterFully Practical – Project-based learning for real-world applicationsWho This Course Is For:Flutter developers interested in integrating AI-powered facial featuresMobile app developers building security or attendance systemsBeginners and intermediates looking to explore Face Recognition in FlutterAnyone who wants to learn offline face recognition with no paid API usageTechnologies Covered:Flutter & DartTensorFlow Lite (TFLite)Google ML Kit Face DetectionFaceNet & MobileFaceNet ModelsReal-time Camera IntegrationImage Picker & Camera PluginsBy 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!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 How Face Recognition Works: Detection, Embeddings & Matching Explained

Section 2: Flutter(Android & IOS): Environment Setup for MacOS

Lecture 3 Install the Flutter SDK

Lecture 4 Install Android Studio

Lecture 5 Install and Setup XCode

Lecture 6 Creating A Flutter Project and Installing in IOS Simulator

Lecture 7 Install the Android Emulator

Section 3: Flutter(Android & IOS): Setup for Windows

Lecture 8 Installing Flutter on Windows

Lecture 9 Installing Android Studio

Lecture 10 Creating Android Virtual Device

Section 4: ImagePicker Flutter: Choosing or Capturing Images in Android & IOS

Lecture 11 Creating a new Flutter Project and building GUI of ImagePicker Application

Lecture 12 Adding Libraries and doing Android & IOS configuration in Flutter

Lecture 13 Choosing Images From Gallery in Flutter

Lecture 14 Capturing Images using Camera in Flutter

Lecture 15 ImagePicker in Flutter Overview

Section 5: Face Detection in Flutter with Images

Lecture 16 Import and Run the Starter Flutter App for Face Recognition

Lecture 17 Exploring the Starter Flutter Code for Face Detection & Recognition

Lecture 18 Adding Face Detection Libraries in Flutter + Android Setup

Lecture 19 Performing Face Detection in Flutter and Logging Results to Console

Lecture 20 Draw Bounding Boxes Around Detected Faces in Flutter

Lecture 21 Creating a FaceDetectorPainter Class in Flutter for Drawing Face Boxes

Section 6: Face Recognition in Flutter with Images

Lecture 22 How to Crop Detected Faces from an Image in Flutter

Lecture 23 Setting Up Face Recognition in Flutter using TensorFlow Lite

Lecture 24 Initialize Face Recognition Model and Generate Face Embeddings in Flutter

Lecture 25 Registering Faces in Local Database with Embeddings in Flutter

Lecture 26 Recognizing Registered Faces in Flutter Using Face Embeddings

Lecture 27 Display Names of Recognized Faces on Screen in Flutter

Section 7: Improving Accuracy and Performance of Face Recognition App in Flutter

Lecture 28 Solving Face Recognition Issues and Setting Matching Threshold in Flutter

Lecture 29 Testing Face Recognition App with Multiple Faces

Lecture 30 Using the FaceNet Model for Face Recognition in Flutter

Lecture 31 Tips to Improve Face Recognition Accuracy in Your Flutter App

Section 8: Behind the Scenes: How Face Recognition Works in Mobile Apps

Lecture 32 What’s Next? Exciting Projects & Ideas

Lecture 33 Passing Input to the Face Recognition Model and Retrieving Output in Flutter

Lecture 34 How Faces Are Stored in the Database for Recognition in Flutter

Lecture 35 Storing Registered Faces and Embeddings in Flutter Database

Section 9: Managing Registered Faces in Flutter

Lecture 36 Creating a Registered Faces List Screen in Flutter

Lecture 37 Coding Registered Faces Screen in Flutter

Section 10: Flutter(Android & IOS): Displaying Live Camera Footage

Lecture 38 Creating new Flutter project and Adding library

Lecture 39 Displaying Live Camera Footage in Flutter

Lecture 40 Getting Frames of Camera Footage One by One in Flutter

Lecture 41 Camera Package Overview

Section 11: Realtime Face Recognition - Setup & Face Detection

Lecture 42 Importing and Running the Real-Time Face Recognition Starter App

Lecture 43 Registration Screen and Displaying Live Camera Feed in Flutter

Lecture 44 Switching Between Front & Rear Cameras in Flutter

Lecture 45 Real-Time Face Detection: Passing Camera Input and Retrieving Faces in Flutter

Lecture 46 Drawing Bounding Boxes Around Detected Faces in Real-Time Flutter App

Section 12: Realtime Face Registration & Recognitions in Flutter

Lecture 47 Cropping Faces from Camera Frames for Recognition in Flutter

Lecture 48 Passing Cropped Faces to the Model and Extracting Face Embeddings in Flutter

Lecture 49 Displaying Face Registration Dialog for Captured Faces in Flutter

Lecture 50 Implementing the Face Registration Dialog Code in Flutter

Lecture 51 Creating a Face Detector Painter for Live Camera Face Recognition in Flutter

Lecture 52 Performing Real-Time Face Recognition in Flutter with Live Camera Feed

Lecture 53 Setting Recognition Threshold and Overview of Real-Time Face Matching in Flutter

Lecture 54 Building a Face Detector Painter for the Recognition Screen in Flutter

Lecture 55 Creating the Registered Faces Screen to Display Saved Users in Flutter

Section 13: Realtime Face Recognition - Best Version

Lecture 56 Face Recognition in Flutter - Best Version

Lecture 57 Registering Faces from Multiple Angles for Improved Recognition in Flutter

Lecture 58 Updating the Face Recognizer Class for Better Accuracy and Performance

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