Android face recognition libraries
Face Recognition library for Android devices is an Android library (module) which includes several face recognition methods.
The Android client for the Kairos Face Recognition API. Includes example Facial Recognition project and SDK jar library.
Android application for gender, age and face recognition using OpenCV and JavaCV libraries
An Android Face Recognition library using NDK/JNI
The Weka ML library for Java is FREE and provides access to various ML algorithms and neural networks. These algorithms can be utilized for a variety of purposes, including facial recognition, which can be done for free unlike APIs like Kairos or Microsoft Azure. Clone this repo, give it a star, and have fun using the facial recognition app!
An Android Library for you to incorporate facial authentication into your app
Face Recognition library for Android devices is an Android library (module) which includes several face recognition methods.
Android Face Recognition App using Microsoft Cognitive Face library
Face recognition android application with opencv and javacv library
University Android project for experimenting with libraries for face detection and recognition.
Legacy university project to test face recognition feasibility on an android basic phone using openCV public library
[WIP] Face recognition sample app using https://github.com/Qualeams/Android-Face-Recognition-with-Deep-Learning-Library
This is a Android application. Implementation of the camera, face++ library call. Make the image of the face feature points detection, recognition, and the production of Q version of graffiti and other functions.
The Android client for the Kairos Face Recognition API. Includes example Facial Recognition project and SDK jar library.
Sample Face Recognition Android app using Firebase MLKit and dlib library.
Driver drowsiness is the most critical cause of road accidents so detection of drowsiness play a vital role in preventing road accidents. We are developing an android app that will alert drivers before an accident occurs. This will reduce the number of road accidents on a road. Drowsiness is a natural phenomenon that happens in human body due to different factors. Machine learning was applied to predict drowsiness and improve drowsiness prediction using facial recognition technology and eye-blink recognition technology. In this app, front camera will take a picture of drowsy driver then this picture will be taken as input. In processing the detected image, we are using OpenCV Library. OpenCV Library uses Haar Cascade Classifier for detection images such as eyes and face. Eyes and face will be the target in this system. This application will be implemented on Android Operating System. Drowsiness detection system will send alert to the driver when the driver feels asleep while driving a car, this can avoid accidents. Driver which is the user in this application, if they close their eyes within one second, the sensor which is the front camera in the smartphone will catch and process this event and then trigger the system to give voice alert to the user. Moreover, if the driver is willing to turn on back camera then it will detect the lane detection violation and will calculate the distance from the vehicle ahead of it. If the distance is too close, then it will generate an alarm. It will also generate an alarm if there is a violation of the lane on the road.
Testing DJL Deep Learning Java Library with a PyTorch Face Recognition model in an Android project.
Visage Vault is an Android application developed using Android Studio that utilizes TensorFlow Lite model and Firebase ML Kit Library to identify faces, record their names, and perform face recognition of recorded images.
Android Face Recognition App using Microsoft Cognitive Face library
Android application capable of scanning barcodes, image labeling, text recognition and face detection. Uses ML kit & CameraX libraries
Android Attendance System built on Java in Android Studio. Uses robust TFLite Face-Recognition models along with MLKit and CameraX libraries to detect and recognize faces, in turn marking their attendance. More features include Adding new employee and Displaying the database
