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Amazon Rekognition Documentation

Amazon Rekognition Face Liveness

Amazon Rekognition Face Liveness is a managed machine learning (ML) feature designed to help developers deter fraud in face-based identity verification. The feature is designed to help you verify that a user is physically present in front of the camera and isn’t a bad actor spoofing the user's face.

Amazon Rekognition Video

Amazon Rekognition Video is a machine learning powered video service that is designed to support both real time streaming video events and stored video analysis. Amazon Rekognition streaming video events is designed to detect objects on video streams from connected cameras. Amazon Rekognition stored video analysis is designed to analyze your videos stored in Amazon S3 and detect objects, landmarks, scenes, celebrities, text, activities, and inappropriate content from your videos stored in Amazon S3. Rekognition Video stored video analysis is also designed to provide facial analysis and facial search capabilities to detect, analyze, and compare faces, and help understand the movement of people in your videos. Each result or detection is paired with a timestamp so that you can create an index for detailed video search, or navigate to a part of the video for further analysis. For objects, faces, text, and people, Rekognition Video stored video analysis also returns bounding box coordinates, which is the specific location of the detection in the frame.

Amazon Rekognition Image

Rekognition Image is a deep learning powered image recognition service that is designed to detect objects, scenes, and faces; extract text; recognize celebrities; and identify inappropriate content in images. It also allows you to search and compare faces. The service is designed to return a confidence score for everything it identifies so that you can make informed decisions about how you want to use the results. In addition, detected faces are returned with bounding box coordinates, which is a rectangular frame that encompasses the face that can be used to locate the position of the face in the image.

Amazon Rekognition Custom Labels

Amazon Rekognition Custom Labels are designed to help you identify the objects and scenes in images that are specific to your business needs.

Rekognition Custom Labels builds off of Rekognition’s capabilities, which are already trained on images across many categories. You can upload a set of training images that are specific to your use case into our console. If your images are already labeled, Rekognition can begin training. If not, you can label them within Rekognition’s labeling interface, or use Amazon SageMaker Ground Truth to label them for you. Once Rekognition begins training from your image set, it can produce a custom image analysis model for you. You can then use your custom model via the Rekognition Custom Labels API and integrate it into your applications.

Additional Information

For additional information about service controls, security features and functionalities, including, as applicable, information about storing, retrieving, modifying, restricting, and deleting data, please see https://docs.aws.amazon.com/index.html. This additional information does not form part of the Documentation for purposes of the AWS Customer Agreement available at http://aws.amazon.com/agreement, or other agreement between you and AWS governing your use of AWS’s services.