⚠️ We recommend customers to explore Amazon Rekognition Face Liveness, a new feature to deter fraud in facial verification, that is now generally available. Face Liveness detects spoofs presented to the camera (e.g. printed photos, digital photos or videos, or 3D masks) and spoofs that bypass the camera (e.g. pre-recorded real or deepfake videos). Face Liveness returns a high-quality selfie frame for downstream Amazon Rekognition Face Matching or Age Estimation analysis. Customers can easily add Face Liveness to their React web, native iOS, and native Android applications using open-source AWS Amplify SDKs. Face Liveness automatically scales up or down based on demand and customers pay only for the face liveness checks performed. No infrastructure management, hardware-specific implementation, or machine learning (ML) expertise is required. Face Liveness uses ML models trained on diverse datasets to support high accuracy across user skin tones, ancestries, and devices. Learn more at aws.amazon.com/rekognition/face-liveness . Code samples and developer documentation here: https://docs.aws.amazon.com/rekognition/latest/dg/face-liveness.html

Check the Liveness Detection Framework, a solution that helps you implement liveness detection mechanisms into your applications by means of an extensible architecture. The solution is available in the AWS Solutions Implementations website and its code is available in a GitHub repo.

• The solution provided here is not secure against all spoofing. Therefore, before deploying to production, thoroughly test it to validate if it meets your security requirements.
• The included client (JavaScript frontend web application) is not optimized for small screens and also lacks refinements. You should use it as a starting point or as a sample of how to implement different clients (the backend is client agnostic).
• The ability to properly monitor a system in production is crucial and the implemented code doesn't provide enough means to do so. Consider using both Amazon CloudWatch Logs and Amazon CloudWatch Metrics to have more visibility of all architecture components before deploying to production.

This package includes the backend and a sample client (JavaScript frontend) of a liveness detection application.

The application asks the user to perform a challenge: the user must move the nose to a random area shown on the screen. In the end, the following is verified: 1. there was one and only one face, 2. the user moved the nose to the target area, and 3. the user rotated the face.

Following there are some screenshots of a user performing the challenge:

Following is the architecture of the application:

1. Configure the AWS Credentials in your environment. Refer to Configuration and credential file settings .

2. Download and install AWS CLI. Refer to Installing the AWS CLI.

3. Download and install AWS SAM CLI. Refer to Installing the AWS SAM CLI .

4. Download and install Docker. Refer to Docker.

5. Download and install Node.js. Refer to Node.js Downloads.

Run the command below to deploy the backend:

sam build --use-container && sam deploy --guided

The command above creates a CloudFormation stack with the following outputs: ApiUrl, StaticWebsiteUrl, and StaticWebsiteBucket. You need those values in the next steps.

1. Enter in the client/ directory.

2. Download the ML models from face-api.js library:

curl -o public/weights/tiny_face_detector_model-shard1.shard -kL https://github.com/justadudewhohacks/face-api.js/blob/a86f011d72124e5fb93e59d5c4ab98f699dd5c9c/weights/tiny_face_detector_model-shard1?raw=true
echo 'f3020debaf078347b5caaff4bf6dce2f379d20bc *public/weights/tiny_face_detector_model-shard1.shard' | shasum -c

curl -o public/weights/tiny_face_detector_model-weights_manifest.json -kL https://github.com/justadudewhohacks/face-api.js/blob/a86f011d72124e5fb93e59d5c4ab98f699dd5c9c/weights/tiny_face_detector_model-weights_manifest.json?raw=true
echo '1f9da0ddb847fcd512cb0511f6d6c90985d011e6 *public/weights/tiny_face_detector_model-weights_manifest.json' | shasum -c

curl -o public/weights/face_landmark_68_model-shard1.shard -kL https://github.com/justadudewhohacks/face-api.js/blob/a86f011d72124e5fb93e59d5c4ab98f699dd5c9c/weights/face_landmark_68_model-shard1?raw=true
echo 'e8b453a3ce2a66e6fa070d4e30cd4e91c911964b *public/weights/face_landmark_68_model-shard1.shard' | shasum -c

curl -o public/weights/face_landmark_68_model-weights_manifest.json -kL https://github.com/justadudewhohacks/face-api.js/blob/a86f011d72124e5fb93e59d5c4ab98f699dd5c9c/weights/face_landmark_68_model-weights_manifest.json?raw=true
echo 'a981c7adfc6366e7b51b6c83b3bb84961a9a4b15 *public/weights/face_landmark_68_model-weights_manifest.json' | shasum -c

3. Change the models' paths in the manifests:

perl -i -pe 's/tiny_face_detector_model-shard1/tiny_face_detector_model-shard1.shard/g' public/weights/tiny_face_detector_model-weights_manifest.json

perl -i -pe 's/face_landmark_68_model-shard1/face_landmark_68_model-shard1.shard/g' public/weights/face_landmark_68_model-weights_manifest.json

4. Open the .env file and replace the value of VUE_APP_API_URL with the API URL (ApiUrl) outputted during the backend deployment.

5. Run the comamand below to build the frontend:

npm install && npm run build

6. Copy the static frontend files with the following command (replace {YOUR_BUCKET} with the bucket name
   • StaticWebsiteBucket - outputted during the backend deployment):

aws s3 cp dist s3://{YOUR_BUCKET}/ --recursive

Open your browser and navigate to the CloudFront URL (StaticWebsiteUrl) outputted during the backend deployment.

If you don't want to continue using the application, take the following steps to clean up its resources and avoid further charges.

Before you can delete a bucket, you must first delete its contents.

1. Sign in to the AWS Management Console and open the Amazon S3 console at https://console.aws.amazon.com/s3.

2. In the Bucket name list, select the option next to the StaticWebsiteBucket and then choose Empty.

3. On the Empty bucket page, confirm that you want to empty the bucket by entering the bucket name into the text field, and then choose Empty.

4. In the Bucket name list, select the option next to the FramesBucket and then choose Empty.

5. On the Empty bucket page, confirm that you want to empty the bucket by entering the bucket name into the text field, and then choose Empty.

AWS CloudFormation makes it easy to clean up resources that the stack created. When you delete the stack, all resources that the stack created are deleted.

1. Open the AWS CloudFormation console at https://console.aws.amazon.com/cloudformation.

2. On the Stacks page in the CloudFormation console, select the stack you deployed during the Backend setup.

3. In the stack details pane, choose Delete.

4. Select Delete stack when prompted.

See CONTRIBUTING for more information.

This library is licensed under the MIT-0 License. See the LICENSE file.