Deploying Lambda function: sessionFunction

Overview

This page describes how to create the sessionFunction Lambda function.
It is used to start a new Amazon Rekognition Face Liveness Session and store relevant data in Amazon S3/DynamoDB.

Step 1: Create the Lambda function

  1. Still in Lambda console, navigate to functions.

  2. Choose Create function.

  3. Under Author from scratch:

    • Function name: sessionFunction
    • Runtime: Node.js 22.x
    • Architecture: x86_64

Other Additional configurations can be left alone.

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  1. Choose Create function.

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Step 2: Add the function code

  1. In the Code tab, replace the default code with the handler from your Amplify project:

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Or copy the handler.js file from:
Amplify/functions/sessionFunction/handler.js

// create session

import {
  CreateFaceLivenessSessionCommand,
  RekognitionClient,
} from "@aws-sdk/client-rekognition";

const clientConfig = {
  region: process.env.AWS_REGION || "us-east-1",
  credentials: {
    accessKeyId: process.env.AWS_ACCESS_KEY_ID,
    secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY,
  },
};

const rekognitionClient = new RekognitionClient(clientConfig);

const createLivenessSession = async (s3Bucket) => {
  const command = new CreateFaceLivenessSessionCommand({
    Settings: {
      OutputConfig: {
        S3Bucket: s3Bucket,
        S3Prefix: "/",
      },
    },
  });

  return await rekognitionClient.send(command);
};
export const handler = async (event) => {
  try {
    console.log("Creating new liveness session...");
    console.log(`S3 Bucket: ${process.env.S3_BUCKET}`);
    console.log(`S3 Prefix: /`);

    const response = await createLivenessSession(process.env.S3_BUCKET);

    console.log(`Liveness session created: ${response.SessionId}`);
    console.log(`Output will be stored in: s3://${process.env.S3_BUCKET}/`);

    return {
      statusCode: 200,
      headers: {
        "Content-Type": "application/json",
        "Access-Control-Allow-Origin": "*", // nếu cần CORS
      },
      body: JSON.stringify({
        sessionId: response.SessionId,
        success: true,
      }),
    };
  } catch (err) {
    console.error("Error creating liveness session:", err);
    return {
      statusCode: 500,
      headers: {
        "Content-Type": "application/json",
        "Access-Control-Allow-Origin": "*",
      },
      body: JSON.stringify({
        success: false,
        error: err.message,
      }),
    };
  }
};

Choose Deploy (or Ctrl + Shift + U) to save your changes.

Step 3: Configure environment variables

  1. In the Configuration tab, choose Environment variables.

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  1. Add the following:
S3_BUCKET=YOUR_S3_BUCKET_NAME
REKOGNITION_COLLECTION=YOUR_REKOGNITION_COLLECTION_NAME
DYNAMO_TABLE=YOUR_DYNAMO_TABLE_NAME

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  1. Save changes.

Step 4: Assign IAM permissions

Your Lambda function needs permission to create a Face Liveness Session and put objects in S3.

  1. Still in the Configuration tab, go to Permissions and click the role name attached to your Lambda function.

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  1. This will open the IAM console — choose Add permissions → Create inline policy.

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  1. Switch to JSON view and paste:
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "rekognition:CreateFaceLivenessSession",
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": ["s3:PutObject", "s3:PutObjectAcl"],
      "Resource": "arn:aws:s3:::YOUR_S3_BUCKET_NAME/*"
    }
  ]
}

Replace YOUR_S3_BUCKET_NAME with your actual bucket name. The arn:aws:s3::: format should exactly match your bucket.

  1. Save the policy and attach it to your Lambda execution role.

At this point, sessionFunction is ready to integrate with your workflow for creating Face Liveness Sessions in Rekognition.