There are 1 repository under rekognition topic.
Unified UI and API for processing and training images for facial recognition.
Homebridge plugin for RTSP Cameras with HSV, motion detection support, Image Rekognition, Web UI to manage/watch streams and WebApp support
Fast object detection, face recognition and S3 upload of ZoneMinder alarms.
Backend and a JavaScript frontend of a liveness detection application.
Home Assistant Object detection with Amazon Rekognition
Real-time image tracking with React, GraphQL, and AWS AppSync
Notes & HowTo's covering the Raspberry Pi, Arduino, ESP8266, ESP32, etc.
Face Attendance (AWS rekognition)
This sample, built using AWS Amplify, is meant to showcase recommended flows when using Amazon Rekognition for Identity Verification.
This repo presents a demo application for realtime livestream video quality monitoring using AWS serverless and AI/ML services.
Example implementation of the Identity Verification using Amazon Rekognition whitepaper.
An intelligent photo gallery built with Drupal 8.
This is a general overview of the Predictions category of Amplify. It shows examples of Machine Learning and AI service integration in a React app with AWS Amplify Predictions category
A demo illustrating Laravel's integration with the AWS suite (e.g. S3, Rekognition, Elastic Transcoder)
👀 A wrapper to easily & quickly integrate popular image & video recognition libraries. Currently supports AWS Rekognition.
An API, storage and face detection for a photo archive web app using serverless framework
A Laravel Package/Facade for the AWS Rekognition API
Example with WebRTC , AWS Rekognition :+1:
Sample app for AWS Serverless Repository - uses Amazon Rekognition to recognize person on the photo
This project contains source code and supporting files for a serverless application which can be used for Computer Vision inferencing using Amazon Rekognition.
Sample demo that shows how to build an AI powered app using Amplify, AppSync, DynamoDB, Cognito and Rekognition.
AWS 뿌시기 - 얼굴분석을 통한 표정에 알맞은 음악 추천 서비스
With Amazon Rekognition Custom Labels, you can easily build and deploy Machine Learning (ML) models to identify custom objects which are specific to your business domain in images without requiring advanced ML knowledge. When combined with Amazon Augmented AI (A2I), you can quickly integrate a ML workflow to capture and label images with a human workforce for model training. As ML lifecycle is an iterative and repetitive process, you need to implement an effective workflow that can provide for continuous model training with new data and automated deployment. Your workflow also needs to be flexible enough to allow for changes without requiring development rework as your business objectives change. Operationalizing an effective and flexible workflow can be resource intensive, especially for customers who have limited machine learning capabilities. In this post, we will use AWS Step Functions, AWS Lambda, and AWS System Manager Parameter Store to automate a configurable ML workflow for Rekognition Custom Labels and A2I. We will provide an overview of the solution and instructions to deploy it with AWS CloudFormation.
Image Proxy for use with Wallets
Examples of using AWS Rekognition from Python
An event-driven app to label images on top of AWS using SST (Serverless Stack), TypeScript, and React.
Lambda function to pull from S3, run image through AWS Rekognition and store in DynamoDB
This demo shows how you can use Amazon IVS's Auto-Record-to-S3 feature in conjunction with Amazon Rekognition to moderate streams.
Application that tries recognize pokémons analyzing an photo (a.k.a pokédex). Built to AWS Cloud.