Amazon Rekognition | Facial Recognition Flashcards
How many faces can I compare against?
Facial Recognition
Amazon Rekognition | Machine Learning
You can compare one face in the source image with up to 15 detected faces in the target image.
What is Facial Recognition?
Facial Recognition
Amazon Rekognition | Machine Learning
Facial recognition is the process of identifying or verifying a person’s identity by searching for their face in a collection of faces. Using facial recognition, you can easily build applications such as multi-factor authentication for bank payments, automated building entry for employees, and more.
What is a face collection and how do I create one?
Facial Recognition
Amazon Rekognition | Machine Learning
A face collection is a searchable index of face feature vectors, owned and managed by you. Using the CreateCollection API, you can easily create a collection in a supported AWS region and get back an Amazon Resource Name (ARN). Each face collection has a unique CollectionId associated with it.
How do I add faces to or delete faces from a face collection?
Facial Recognition
Amazon Rekognition | Machine Learning
To add a face to an existing face collection, use the IndexFaces API. This API accepts an image in the form of an S3 object or image byte array and adds a vector representation of the faces detected to the face collection. IndexFaces also returns a unique FaceId and face bounding box for each of the faces added.
To delete a face to an existing face collection, use the DeleteFaces API. This API operates on the face collection supplied (using a CollectionId) and removes the entries corresponding to the list of FaceIds. For more information on adding and deleting faces , please refer to our Managing Collections example.
How do I search for a face within a face collection?
Facial Recognition
Amazon Rekognition | Machine Learning
Once you have created an indexed collection of faces, you can search for a face within it using either an image (SearchFaceByImage) or a FaceId (SearchFaces). These APIs take in an input face and return a set of faces that match, ordered by similarity score with the highest similarity first. For more details, please refer to our Searching Faces example.
How is Facial Recognition different for video analysis?
Facial Recognition
Amazon Rekognition | Machine Learning
Rekognition Video allows you to perform real time face searches against collections with tens of millions of faces. First, you create a face collection, where you can store faces, which are vector representations of facial features. Rekognition then searches the face collection for visually similar faces throughout your video. Rekognition will return a confidence score for each of the faces in your video, so you can display likely matches in your application. For security and surveillance applications, this helps identify persons of interest against a collection of millions of faces in real-time, enabling timely and accurate crime prevention.
