Guide to Learning the Amazon Rekognition Platform and API


Amazon Rekognition is a service that allows developers to add image and video analysis to their applications. With it, you can detect objects, scenes, faces, identify celebrities, and even spot potentially unsafe content. This guide will walk you through the basics of the platform and its API.


  1. AWS Account: Before diving in, make sure you have an Amazon Web Services (AWS) account.
  2. AWS CLI: Install the AWS Command Line Interface (CLI). It’s a handy tool for interacting with AWS services.
  3. Programming experience: Familiarity with a programming language like Python, JavaScript, or Java will be helpful.

1. Getting Started

1.1. Setting Up Your Environment

  1. IAM: Before you begin, set up an IAM user in the AWS Console with the necessary permissions to access Rekognition. Always avoid using root user credentials.
  2. AWS SDK: Amazon Rekognition is accessible via the AWS SDKs available for various languages. Depending on your language of choice, you may want to set up the SDK. For this guide, we’ll use Python with the Boto3 library.

1.2. Exploring the Console

Amazon Rekognition provides a web-based console where you can try out many of its features without writing any code. Before diving deep into the API:

  1. Navigate to the Amazon Rekognition Console in your AWS Dashboard.
  2. Explore the various options available, like image and video analysis.

2. Diving into the API

2.1. Analyzing Images

There are several functions provided by the Rekognition API to analyze images:

  1. DetectLabels: Detect objects, people, scenes, and activities.
  2. DetectFaces: Detect facial features and attributes.
  3. CompareFaces: Compare two faces and see how similar they are.
  4. RecognizeCelebrities: Identify famous individuals in your images.

Example: Detecting Labels using Python and Boto3

import boto3 client = boto3.client('rekognition') response = client.detect_labels( Image={ 'S3Object': { 'Bucket': 'your-bucket-name', 'Name': 'your-image-name.jpg' } }, MaxLabels=10 ) print(response['Labels'])

2.2. Working with Videos

Rekognition’s video analysis functions similarly to its image functions, but due to the asynchronous nature of video analysis, you’ll often start an analysis task and get results later.

Some key video functions include:

  1. StartLabelDetection
    • Purpose: Initiates the asynchronous detection of labels in a video.
    • How it Works: Once started, this function processes video frames to identify objects, scenes, and activities, such as “bicycle,” “tree,” or “walking.”
    • Applications: Can be used in video surveillance for object monitoring, content generation for tagging video content, and more.
  2. StartFaceDetection
    • Purpose: Begins the asynchronous detection of faces within a video.
    • How it Works: This function scans video frames to identify and locate faces. For each detected face, it returns the position and facial attributes such as age range, emotions, and gender.
    • Applications: Useful in situations like crowd monitoring, audience reactions during events, and security applications.
  3. GetLabelDetection
    • Purpose: Retrieves the results of the label detection operation once it’s complete.
    • How it Works: After you initiate label detection using StartLabelDetection, you can use this function to obtain the detected labels, their confidence scores, and their timings in the video.
    • Applications: Post-processing of video data, generating insights or reports based on video content, and refining content recommendation systems.
  4. GetFaceDetection
    • Purpose: Retrieves the results of the face detection operation once it’s done.
    • How it Works: After starting face detection with StartFaceDetection, this function allows you to get details of the detected faces, their attributes, and their timings in the video.
    • Applications: Analyzing audience engagement, monitoring restricted areas for unauthorized access, and in-depth video analytics.

3. Advanced Features

3.1. Custom Labels

With Amazon Rekognition Custom Labels, you can train your own machine learning model to detect specific items in images tailored to your needs.

  1. Create a dataset: You’ll need a labeled dataset.
  2. Train your model: Use the Rekognition console to train your model.
  3. Test & deploy: Once your model is trained, you can test and deploy it.

3.2. Face Collections

You can create collections of faces, which lets you search for faces in an image that match those in your collection. Useful functions include:

  1. CreateCollection
    • Purpose: Creates a new collection of faces.
    • How it Works: A collection is a container for storing face metadata. Once you create a collection, you can add face data to it.
    • Applications: Building a face database for facial recognition systems, creating a whitelist/blacklist of faces for security systems.
  2. IndexFaces
    • Purpose: Adds face data to the specified collection.
    • How it Works: This function detects faces in an input image and adds them to the specified collection. It returns face records for each face detected, which includes a face ID and a bounding box.
    • Applications: Populating a database for a facial recognition system, storing face data for repeat visitors or customers.
  3. SearchFaces
    • Purpose: Searches for matching faces in the specified collection.
    • How it Works: You provide a source face, and the function searches for faces in the collection that look similar. It returns a ranked list of faces with similarity scores.
    • Applications: Finding a person of interest in a database, verifying identity using facial data, and ensuring only authorized personnel access specific areas.

4. Best Practices & Tips

  1. Stay within limits: Rekognition has API rate limits. Familiarize yourself with them to avoid throttling.
  2. Optimize costs: Only request features you need. For instance, if you only need object labels, don’t request face detection.
  3. Handle failures: Always add error handling in your code to manage potential API failures.

5. Wrapping Up

Amazon Rekognition offers a powerful set of tools for developers interested in image and video analysis. Like any AWS service, the key is understanding how its different features fit together and mapping them to your specific needs. With hands-on experimentation and real-world applications, you’ll become proficient in no time. Happy coding!