AWS Rekognition Getting Started: Account, Keys, & First Request (2026)

Getting started overview

To initiate development with AWS Rekognition, the process involves several foundational steps: establishing an AWS account, creating an Identity and Access Management (IAM) user, configuring programmatic access, and then making your first API call. This guide outlines the minimum requirements to interact with Rekognition services, focusing on secure access and a functional initial request.

AWS Rekognition provides a RESTful API that can be accessed directly or, more commonly, through one of its AWS SDKs. These SDKs abstract the complexities of signing requests and handling responses, offering language-specific interfaces for popular programming environments like Python, Java, and JavaScript.

The core process can be summarized in these steps:

1. Sign up for an AWS account.
2. Create an IAM user with Rekognition permissions.
3. Generate access keys for the IAM user.
4. Install an AWS SDK (e.g., Boto3 for Python).
5. Configure your development environment with credentials.
6. Write and execute your first Rekognition API request.

This structure ensures secure access and provides a repeatable pattern for integrating Rekognition into applications.

Create an account and get keys

Accessing AWS Rekognition requires an active AWS account and programmatic credentials. Follow these steps to set up your environment:

1. Sign up for an AWS Account

If you do not have an AWS account, begin by creating one on the AWS website. The signup process requires a valid email address, payment method, and phone number for verification. New accounts are often eligible for the AWS Free Tier, which includes a limited amount of Rekognition usage for the first 12 months.

2. Create an IAM User

Using the AWS root account for programmatic access is discouraged due to security risks. Instead, create an IAM user:

1. Navigate to the IAM console.
2. In the navigation pane, choose Users, then Add user.
3. Enter a User name (e.g., rekognition-dev-user).
4. For AWS credential type, select Access key - Programmatic access. This generates an access key ID and secret access key for the user.
5. Click Next: Permissions.

3. Attach Policies (Grant Permissions)

To grant the IAM user permissions to access Rekognition, attach a suitable policy:

1. On the Set permissions page, select Attach existing policies directly.
2. In the policy list, search for and select AmazonRekognitionFullAccess. This policy grants full access to Rekognition actions. For production environments, it is recommended to use fine-grained permissions.
3. Click Next: Tags (optional), then Next: Review.
4. Review your choices and click Create user.

4. Obtain Access Keys

After creating the user, AWS will display the Access key ID and Secret access key. It is crucial to immediately copy and securely store these credentials, as the secret access key will not be displayed again. If lost, you will need to generate new keys.

5. Configure AWS CLI or SDK

You can configure your access keys in several ways:

• AWS CLI: Install the AWS Command Line Interface and run aws configure, providing your Access Key ID, Secret Access Key, default region, and default output format.
• Environment Variables: Set AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables. This is often used for quick testing but less secure for long-term projects.
• SDK Configuration: Most AWS SDKs automatically look for credentials in standard locations (e.g., ~/.aws/credentials file or environment variables). Refer to the specific SDK documentation for detailed configuration instructions.

Your first request

This example demonstrates how to perform a basic image analysis using Python and the Boto3 SDK. Ensure you have Python installed and Boto3 configured with your AWS credentials.

Prerequisites

• Python 3.x installed.
• Boto3 installed: pip install boto3
• AWS credentials configured in your environment (e.g., via aws configure).
• An image file (e.g., my_image.jpg) in the same directory as your Python script, or accessible via an S3 bucket.

Example: Detecting Labels in an Image

This Python script calls the detect_labels API to identify objects and scenes in an image.

import boto3
import json

def detect_labels_local_image(photo):
    client = boto3.client('rekognition', region_name='us-east-1') # Specify your desired region

    with open(photo, 'rb') as image: # Open image in binary read mode
        response = client.detect_labels(
            Image={'Bytes': image.read()},
            MaxLabels=10, # Maximum number of labels to return
            MinConfidence=75 # Minimum confidence level for labels
        )

    print('Detected labels for ' + photo)
    print(json.dumps(response['Labels'], indent=2))
    return len(response['Labels'])

if __name__ == '__main__':
    photo = 'my_image.jpg' # Replace with your image file name
    label_count = detect_labels_local_image(photo)
    print(f"Found {label_count} labels in the image.")

To run this code:

1. Save the code as a .py file (e.g., rekognition_test.py).
2. Ensure your my_image.jpg file is present or update the photo variable to the correct path.
3. Execute from your terminal: python rekognition_test.py.

The output will be a JSON array of detected labels, including confidence scores and hierarchical parent categories, as described in the Detect Labels API documentation.

Alternative: Using an S3 Bucket Image

For images stored in an Amazon S3 bucket, modify the detect_labels call:

import boto3
import json

def detect_labels_s3_image(bucket, photo):
    client = boto3.client('rekognition', region_name='us-east-1')

    response = client.detect_labels(
        Image={'S3Object': {'Bucket': bucket, 'Name': photo}},
        MaxLabels=10,
        MinConfidence=75
    )

    print('Detected labels for ' + photo + ' in bucket ' + bucket)
    print(json.dumps(response['Labels'], indent=2))
    return len(response['Labels'])

if __name__ == '__main__':
    s3_bucket = 'your-s3-bucket-name'  # Replace with your S3 bucket name
    s3_photo = 'image_in_s3.jpg'      # Replace with your image key in S3
    label_count = detect_labels_s3_image(s3_bucket, s3_photo)
    print(f"Found {label_count} labels in the S3 image.")

Ensure your IAM user has permissions to access the specified S3 bucket (e.g., s3:GetObject). For details on S3 permissions, refer to the Amazon S3 access control documentation.

Common next steps

After successfully making your first request, consider these common next steps to expand your use of AWS Rekognition:

• Explore Other APIs: Experiment with other Rekognition APIs beyond DetectLabels, such as DetectFaces, DetectText, or RecognizeCelebrities, based on your application needs.
• Video Analysis: Begin integrating Rekognition for video analysis tasks. This typically involves using Amazon S3 for video storage and Amazon SNS/SQS for asynchronous processing notifications.
• Custom Labels: For specialized object detection not covered by pre-trained models, explore Rekognition Custom Labels. This involves providing your own training images and labels to create a custom model.
• Error Handling: Implement robust error handling in your code to manage API limits, invalid inputs, and service unavailability. The AWS Rekognition Developer Guide details common errors.
• Asynchronous Operations: For long-running tasks like video analysis, Rekognition uses asynchronous operations. Learn about managing job queues and processing results via Amazon SQS or SNS.
• Security Best Practices: Review AWS security best practices for Rekognition, including using IAM roles instead of long-lived access keys where possible, and encrypting data at rest and in transit.
• Cost Management: Monitor your Rekognition usage and understand the pricing model to manage costs effectively. AWS provides tools like AWS Cost Explorer for this purpose.

Troubleshooting the first call

When encountering issues with your initial AWS Rekognition API calls, consider these common problems and solutions:

For more detailed error codes and troubleshooting guidance, consult the AWS Rekognition Developer Guide on error handling.