Face Recognition and Detection in Android 16- The 2025 Guide

Access course resources via the resources zip to build image picker, face recognition with images, and real-time face recognition apps in Java or Kotlin.

Build your first Android project with a GUI, an image view, and two buttons to pick from gallery or capture with camera, displaying the image for face recognition.

Learn to select images from the gallery in an Android app using an activity result launcher, handling the image uri, and displaying it in an image view.

Add an if block in on activity result to confirm an image selection from the gallery. Check the result code is ok, then display the image.

Capture images in Android apps by adding camera and storage permissions, requesting them at runtime, launching the camera intent, and displaying the captured image in an image view.

Convert an image URI to bitmap in Android, rotate for portrait orientation, and feed the bitmap to a facial recognition model after selecting from gallery or camera.

Implement an image picker in Android by selecting from gallery or capturing with the camera, using intents, handling permissions, and displaying the resulting bitmap in an image view.

Build an android face recognition app that detects and crops faces in gallery or camera, generates embeddings, and registers or recognizes faces with sqlite and models: face net and facenet.

Set up an Android Studio project for face detection by cloning the starter app, register faces via gallery or camera, and recognize them with face net models.

This lecture demonstrates the GUI of a face recognition Android app, outlining main, register, and recognition activities and the image capture or selection flows, bitmap conversion, rotation correction, and display.

Register faces by selecting images, detect faces with ML Kit in Android, store the detected faces for comparison, and initialize a high-accuracy detector via build.gradle and manifest.

Learn to detect faces in Android apps by converting gallery or camera images to input images, running a face detector, and extracting face locations for cropping and recognition.

Draw red rectangles around detected faces in Android by creating a mutable bitmap, obtaining a canvas, and drawing rectangles with a paint stroke from the detector's bounds, using ML Kit.

Configure ML Kit face detector options, convert bitmap to input image format, detect faces, draw bounding boxes on a mutable bitmap with a canvas, and display the result.

Crop each detected face using the bounding rectangle, pass the cropped face to the recognition model to obtain embeddings, and label faces for registration. Handle multiple faces in one image.

Learn how TensorFlow Lite enables on-device machine learning for android, converting models to tf lite, loading them with the interpreter, and delivering low latency with offline privacy.

Explore TensorFlow Lite quantization and how lowering variable precision reduces model size and latency in Android apps, with tradeoffs to accuracy across mobile networks like MobileNet, ResNet, and Inception.

Load the face recognition model in Android by adding tf lite models to the assets folder, initialize a face classifier with tf lite face recognition, and handle io exceptions.

analyze tflite facenet and mobile face net with neutron.app to determine input sizes and embedding outputs, 160 by 160 and 512 versus 112 by 112 and 192.

Pass cropped faces in bitmap format to the recognize image method to obtain an embedding from the face recognition model, then register it with a label.

Present a dialog to label each detected face, crop and scale the image, pass the embedding to the face recognition model, and store the name and embedding in a hashmap.

Register faces globally by displaying the selected image, drawing rectangles around faces, and storing them in a shared hash map in the main activity for cross-screen recognition.

Register faces for Android facial recognition by capturing or selecting images, detecting faces, cropping them, generating embeddings, and storing the name in a hash map.

Learn how an android face recognition app detects faces, crops them, computes embeddings, and compares them with registered faces to recognize the closest match and report the distance.

Displays registered faces on screen in Android by drawing a name and a red rectangle when recognition distance is under one, with adjustable thresholds.

Select or capture an image, detect faces with ML Kit, and crop each face. Then generate embeddings, compare to registered faces, and display names on the image for all faces.

Learn to use Mobile FaceNet in Android apps by updating input to 112 by 112 and output to 192, compare with FaceNet, and test on emulator or devices.

Load a TensorFlow Lite model in an Android app using the TensorFlow Lite face recognition class by loading the model from assets, initializing the interpreter, and preparing input buffers.

Pass a cropped face bitmap to a tflite model by converting it to a byte buffer, obtain an embedding, compare with registered embeddings, and return a recognition object.

Load the model, extract the current image embedding, and compare it to each registered face to find the nearest match by distance.

Register faces by creating a database with a face table that stores names and embeddings, using a DB helper to insert and fetch faces for persistent recognition.

Store face embeddings in a database using a db helper, load registered faces into a hashmap at startup, and register faces through insert face for Android recognition.

Register faces from gallery, name them, and verify recognition against new images with distance metrics. Store and retrieve faces from the database to ensure consistent recognition across app restarts.

Explore how the app registers and stores face data in an Android SQLite database, including creating the faces table, converting embedding arrays to strings, and inserting name and embedding records.

Retrieve all registered faces from the database, convert embedding strings to 2d float arrays, and build a HashMap of names to recognition objects for tflite face recognition.

Build a real-time Android face recognition app that streams live video, detects and crops faces, registers them with SQLite, and recognizes by embeddings using FaceNet and MobileFaceNet.

Set up an android project for real-time face recognition using live camera footage. Capture frames, crop detected faces, and recognize them with TensorFlow Lite and ML Kit.

Display live camera footage in an Android app, switch between front and back cameras, and capture frames as bitmaps for face detection and recognition after handling dynamic camera permission.

Capture live camera frames on Android using an own image available listener, convert frames to bitmaps, and feed them to face detection and recognition models one at a time.

Convert each live camera frame to a bitmap and pass it to ML Kit face detector, configuring accurate performance mode and disabling classification and landmarks, processing frames at a time.

Load the FaceNet model from assets via TensorFlow Lite, convert live camera frames to bitmaps, detect and crop faces to 160 by 160, then recognize or register each face.

Register faces in an Android app by capturing live camera frames, detecting and cropping faces, and storing them with names in a hash map for recognition.

Register faces using the live camera, then recognize them in real time by comparing embeddings with a face net model, and display a bounding rectangle with the name and confidence.

Detect and recognize faces in real-time from live camera frames, map results to registered names, and render labeled bounding boxes on an overlay view above the camera feed.

Test realtime face recognition on an Android app with camera permission, front and back cameras; register faces with the plus icon and see predictions switch from unknown to Hamza.

Fine-tune face cropping by adjusting the left top and height to remove extra area (subtract 30 from height) and tune the distance threshold to improve recognition on the live camera.

Perform real-time face recognition on Android with live camera footage and a tracker that draws names on faces. Register new faces via the plus icon and a name dialog.

Switch between front and back cameras in an Android face recognition app by toggling the use facing variable and restarting the activity, with front camera adjustment for face coordinates.

Switch to the mobile face net model in an Android real-time face recognition app, updating input size to 112 by 112 and embedding size to 192 for live camera footage.

Store registered face data in a database to persist real-time recognition in Android by using a DB helper for saving and retrieving faces with name and embedding.

Register faces in the database via a db helper, storing name and embedding in a faces table, and load all registered faces for real-time recognition.

Register a face named Humza and test real-time face recognition on Android, then verify that stored faces persist in the database and are retrieved when reopening the app.