WHAT IS FACE RECOGNITION ?
A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces..

Amazon Rekognition is directly integrated with Amazon Augmented AI (Amazon A2I) so you can easily route low confidence predictions from Amazon Rekognition Image to human reviewers. Using the Amazon Rekognition API for content moderation or the Amazon A2I console, you can specify the conditions under which Amazon A2I routes predictions to reviewers, which can be either a confidence threshold or a random sampling percentage.

Amazon Rekognition is a cloud-based software as a service (SaaS) computer vision platform that was launched in 2016. It has been sold to, and used by a number of United States government agencies, including U.S. Immigration and Customs Enforcement (ICE) and Orlando, Florida police, as well as private entities.

Capability

Rekognition provides a number of computer vision capabilities, which can be divided into two categories: Algorithms that are pre-trained on data collected by Amazon or its partners, and algorithms that a user can train on a custom dataset. As of July 2019, Rekognition provides the following computer vision capabilities.

Algorithms that a user can train on a custom dataset

SearchFaces enables users to import a database of images with pre-labeled faces, to train a machine learning model on this database, and to expose the model as a cloud service with an API. Then, the user can post new images to the API and receive information about the faces in the image. The API can be used to expose a number of capabilities, including identifying faces of known people, comparing faces, and finding similar faces in a database. Face-based user verification

Controversy regarding facial analysis

Racial and gender bias
In 2018, MIT researchers Joy Buolamwini and Timnit Gebru published a study called Gender Shades.In this study, a set of images was collected, and faces in the images were labeled with face position, gender, and skin tone information. The images were run through SaaS facial recognition platforms from Face++, IBM, and Microsoft. In all three of these platforms, the classifiers performed best on male faces (with error rates on female faces being 8.1% to 20.6% higher than error rates on male faces), and they performed worst on dark female faces (with error rates ranging from 20.8% to 30.4%). The authors hypothesized that this discrepancy is due principally to Megvii, IBM, and Microsoft having more light males than dark females in their training data, i.e. dataset bias.