See the blog post for more information.
This repository contains an end to end example (implemented in a Jupyter notebook) how to build a face search system using Amazon Rekognition.
Amazon Rekognition enables you to achieve very high face search accuracy with a single face image. In some cases, you can use multiple images of the same person's face to create user vectors and improve accuracy even further. This is especially helpful when images have variations in lighting, poses, and appearances.
This will guide you through creating a collection, storing face vectors in that collection, aggregating those face vectors into user vectors, and then comparing the results of searching against those individual face vectors and user vectors.
NOTE: You can run the notebook in SageMaker Studio, JupyterLab, or on your local machine
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.