Flutter Face Recognition - Build Attendance & Security Apps

Set up the Flutter SDK on macOS by downloading the zip, extracting it into a development folder, adding Flutter to your PATH, and verifying the installation with flutter version.

Install Android Studio to build Flutter applications by downloading the latest version from developer.android.com, installing the Flutter plugin, and enabling Flutter project creation in Android Studio.

Install and set up Xcode on a Mac to run flutter projects on iOS, using Android Studio with Xcode and configuring command line tools from the App Store.

Create a new Flutter project in Android Studio, selecting Flutter and naming the app, then install the default counter app and run it on both iOS Simulator and Android Emulator.

Install the starter app by creating and launching an Android emulator, selecting pixel seven with Android 14, downloading the Android 14 zip, and installing the app.

Install flutter on windows by downloading the flutter sdk, unzip, place it in the system, add the flutter bin path to the environment variables, and verify with flutter doctor.

Install android studio to develop flutter apps, set up flutter and dart plugins, configure sdk command line tools, and accept licenses to complete flutter doctor.

Create an Android virtual device in Android Studio using the Virtual Device Manager, choose Pixel 7 Pro, and test apps on the Android emulator.

Create a new Flutter project and build a GUI that lets users choose an image from the gallery or capture one with the camera, then display it in the app.

Add the image picker dependency in pubspec.yaml and run pub get; configure iOS Info.plist permissions for photo library, camera, and microphone, and set Android minSdkVersion to 21.

Learn to use the image picker library in Flutter to select images from the gallery or capture them with the camera, then display the chosen image in the UI.

Capture images in a Flutter app by opening the camera with a long-press. Use the image picker library to switch from gallery to camera and display the captured image.

Master Flutter image handling by selecting images from the gallery or capturing with the camera, then display the chosen image in the UI with an image widget.

Build a Flutter face recognition app that registers faces from gallery or camera, crops them, and recognizes with face net and mobile face net models for attendance and security.

Set up a Flutter face recognition project by cloning starter code from GitHub, running the app in a simulator, and implementing registration and recognition workflows using gallery or camera images.

Add ML Kit face detection to a flutter app, detect and crop faces from images, name them in a dialog, and save to the database with Android and iOS setup.

Set up iOS for flutter with a minimum deployment target of 12. Use Xcode 13+ and Swift 5, exclude armv7, and verify settings in iOS module.

Configure Android SDKs for the Flutter face detection library by setting compile SDK to 33, min to 26, and target to 33, then run on an Android emulator.

Explore Flutter face detection by converting images to input format, initializing an ML Kit face detector with options, and processing images to yield face bounding boxes.

Draw rectangles around detected faces in Flutter by using a custom paint widget, drawing the image on a canvas, and rendering face bounding boxes from the detection model.

Learn to draw red bordered rectangles around detected faces using a custom face painter in Flutter, by initializing a face detector, processing images, and updating the UI with results.

Crop detected faces from the original image in Flutter to generate face embeddings for recognition, using bounding boxes, image conversion, and careful bounds checks.

Add and load a TensorFlow Lite face recognition model in a Flutter app, configure assets folder and dependencies, then set up DB and ML folders to register and recognize faces.

Load a Tflite face recognition model in Flutter, pass cropped faces to the recognizer, and obtain embeddings to register names and store embeddings in a database.

Register faces in the Flutter face recognition app by cropping detected faces, converting them to Uint8 list, and showing a registration dialog to save names and embeddings in the database.

Show how to implement a face registration dialog in Flutter, with image preview, name input, and register button that saves the embedding to the database and shows a confirmation.

In this Flutter lecture, register faces and perform recognition by detecting and cropping faces, generating embeddings, and comparing them to registered embeddings to identify the closest match.

Learn how to display recognized face names on the image by passing recognition objects to the face painter and rendering names at the top left of each rectangle.

Validate face recognitions by using the recognition distance to decide accuracy, override the name to unknown when the distance exceeds 1.25, and test with unregistered faces.

Load tflite models in Flutter using the tflite flutter library, pass input and retrieve output with a recognizer class and TensorFlow Lite interpreter loaded from assets.

Pass the cropped face as a 112 by 112 float array to the tflite model in Flutter and receive a 192-element embedding.

Open and populate the Flutter database by using the database helper to create the table, insert and query faces, load embeddings, and build a registered faces map for recognition.

Register new faces in Flutter database by saving name and embedding, converting embedding list to a comma-separated string, and inserting the row; load faces and compare embeddings with find nearest.

Explore how the app compares a current face embedding to registered embeddings with find nearest function, computing Euclidean distance to identify the closest match and return its name and distance.

Learn to implement the FaceNet model in Flutter for improved face recognition, upgrading from Mobile FaceNet to a 160 input size and 512 output, and register and test faces.

Create a new flutter project and add the camera package to display live camera footage. Configure dependencies and iOS/Android permissions, including minimum sdk version 21, for emulator runs.

Build a real-time Flutter live feed app that displays camera footage and passes frames to machine learning models, setting up Android and iOS with the camera package and required permissions.

Learn to display live camera footage in a Flutter app using the camera package by configuring available cameras, initializing a camera controller, and rendering a CameraPreview in a simplified UI.

Capture live camera frames one by one in Flutter, streaming each camera image to a machine learning model for real-time inference and displaying the results on screen.

Learn to display live camera footage in a Flutter app using the camera package. Configure permissions, initialize cameras, and stream frames with a camera controller and preview.

Build a real-time flutter app that streams live camera footage, detects and crops faces, registers them by name with a dialog, and recognizes them via face.net and mobile face net.

Set up a real-time Flutter face recognition app using starter code from GitHub, import it in Android Studio, and run on an Android emulator with live camera preview.

Explore how the flutter face recognition app uses Google ML Kit for face detection, TF Lite Flutter for models, and Sqf Lite for registering faces with a live camera preview.

Learn how to initialize a Flutter camera controller, switch cameras, display live footage, and stream frames with a busy flag to support face detection and recognition.

Toggle camera direction with an icon button to switch between front and back cameras, stop the image stream, and reinitialize the camera to display live footage.

Detect faces in Flutter with live camera footage by configuring a Google ML Kit face detector with fast mode, converting frames to input images, and processing to obtain bounding boxes.

Overlay a transparent widget on the live camera preview to draw rectangles around detected faces. Render these rectangles with a custom paint widget, updating per frame.

Render real-time face bounding boxes on the live camera feed by applying the face detector painter over a custom paint widget, scaling coordinates for front and back cameras.