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rekognition_index_faces

Detects faces in the input image and adds them to the specified collection

Description

Detects faces in the input image and adds them to the specified collection.

Amazon Rekognition doesn't save the actual faces that are detected. Instead, the underlying detection algorithm first detects the faces in the input image. For each face, the algorithm extracts facial features into a feature vector, and stores it in the backend database. Amazon Rekognition uses feature vectors when it performs face match and search operations using the search_faces and search_faces_by_image operations.

For more information, see Adding Faces to a Collection in the Amazon Rekognition Developer Guide.

To get the number of faces in a collection, call describe_collection.

If you're using version 1.0 of the face detection model, index_faces indexes the 15 largest faces in the input image. Later versions of the face detection model index the 100 largest faces in the input image.

If you're using version 4 or later of the face model, image orientation information is not returned in the OrientationCorrection field.

To determine which version of the model you're using, call describe_collection and supply the collection ID. You can also get the model version from the value of FaceModelVersion in the response from index_faces

For more information, see Model Versioning in the Amazon Rekognition Developer Guide.

If you provide the optional ExternalImageId for the input image you provided, Amazon Rekognition associates this ID with all faces that it detects. When you call the list_faces operation, the response returns the external ID. You can use this external image ID to create a client-side index to associate the faces with each image. You can then use the index to find all faces in an image.

You can specify the maximum number of faces to index with the MaxFaces input parameter. This is useful when you want to index the largest faces in an image and don't want to index smaller faces, such as those belonging to people standing in the background.

The QualityFilter input parameter allows you to filter out detected faces that don’t meet a required quality bar. The quality bar is based on a variety of common use cases. By default, index_faces chooses the quality bar that's used to filter faces. You can also explicitly choose the quality bar. Use QualityFilter, to set the quality bar by specifying LOW, MEDIUM, or HIGH. If you do not want to filter detected faces, specify NONE.

To use quality filtering, you need a collection associated with version 3 of the face model or higher. To get the version of the face model associated with a collection, call describe_collection.

Information about faces detected in an image, but not indexed, is returned in an array of UnindexedFace objects, UnindexedFaces. Faces aren't indexed for reasons such as:

  • The number of faces detected exceeds the value of the MaxFaces request parameter.

  • The face is too small compared to the image dimensions.

  • The face is too blurry.

  • The image is too dark.

  • The face has an extreme pose.

  • The face doesn’t have enough detail to be suitable for face search.

In response, the index_faces operation returns an array of metadata for all detected faces, FaceRecords. This includes:

  • The bounding box, BoundingBox, of the detected face.

  • A confidence value, Confidence, which indicates the confidence that the bounding box contains a face.

  • A face ID, FaceId, assigned by the service for each face that's detected and stored.

  • An image ID, ImageId, assigned by the service for the input image.

If you request all facial attributes (by using the detectionAttributes parameter), Amazon Rekognition returns detailed facial attributes, such as facial landmarks (for example, location of eye and mouth) and other facial attributes. If you provide the same image, specify the same collection, and use the same external ID in the index_faces operation, Amazon Rekognition doesn't save duplicate face metadata.

The input image is passed either as base64-encoded image bytes, or as a reference to an image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes isn't supported. The image must be formatted as a PNG or JPEG file.

This operation requires permissions to perform the rekognition:IndexFaces action.

Usage

rekognition_index_faces(CollectionId, Image, ExternalImageId,
  DetectionAttributes, MaxFaces, QualityFilter)

Arguments

CollectionId

[required] The ID of an existing collection to which you want to add the faces that are detected in the input images.
Image

[required] The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing base64-encoded image bytes isn't supported.

If you are using an AWS SDK to call Amazon Rekognition, you might not need to base64-encode image bytes passed using the Bytes field. For more information, see Images in the Amazon Rekognition developer guide.
ExternalImageId

The ID you want to assign to all the faces detected in the image.
DetectionAttributes

An array of facial attributes that you want to be returned. This can be the default list of attributes or all attributes. If you don't specify a value for Attributes or if you specify ["DEFAULT"], the API returns the following subset of facial attributes: BoundingBox, Confidence, Pose, Quality, and Landmarks. If you provide ["ALL"], all facial attributes are returned, but the operation takes longer to complete.

If you provide both, ["ALL", "DEFAULT"], the service uses a logical AND operator to determine which attributes to return (in this case, all attributes).
MaxFaces

The maximum number of faces to index. The value of MaxFaces must be greater than or equal to 1. index_faces returns no more than 100 detected faces in an image, even if you specify a larger value for MaxFaces.

If index_faces detects more faces than the value of MaxFaces, the faces with the lowest quality are filtered out first. If there are still more faces than the value of MaxFaces, the faces with the smallest bounding boxes are filtered out (up to the number that's needed to satisfy the value of MaxFaces). Information about the unindexed faces is available in the UnindexedFaces array.

The faces that are returned by index_faces are sorted by the largest face bounding box size to the smallest size, in descending order.

MaxFaces can be used with a collection associated with any version of the face model.
QualityFilter

A filter that specifies a quality bar for how much filtering is done to identify faces. Filtered faces aren't indexed. If you specify AUTO, Amazon Rekognition chooses the quality bar. If you specify LOW, MEDIUM, or HIGH, filtering removes all faces that don’t meet the chosen quality bar. The default value is AUTO. The quality bar is based on a variety of common use cases. Low-quality detections can occur for a number of reasons. Some examples are an object that's misidentified as a face, a face that's too blurry, or a face with a pose that's too extreme to use. If you specify NONE, no filtering is performed.

To use quality filtering, the collection you are using must be associated with version 3 of the face model or higher.

Value

A list with the following syntax:

list(
  FaceRecords = list(
    list(
      Face = list(
        FaceId = "string",
        BoundingBox = list(
          Width = 123.0,
          Height = 123.0,
          Left = 123.0,
          Top = 123.0
        ),
        ImageId = "string",
        ExternalImageId = "string",
        Confidence = 123.0
      ),
      FaceDetail = list(
        BoundingBox = list(
          Width = 123.0,
          Height = 123.0,
          Left = 123.0,
          Top = 123.0
        ),
        AgeRange = list(
          Low = 123,
          High = 123
        ),
        Smile = list(
          Value = TRUE|FALSE,
          Confidence = 123.0
        ),
        Eyeglasses = list(
          Value = TRUE|FALSE,
          Confidence = 123.0
        ),
        Sunglasses = list(
          Value = TRUE|FALSE,
          Confidence = 123.0
        ),
        Gender = list(
          Value = "Male"|"Female",
          Confidence = 123.0
        ),
        Beard = list(
          Value = TRUE|FALSE,
          Confidence = 123.0
        ),
        Mustache = list(
          Value = TRUE|FALSE,
          Confidence = 123.0
        ),
        EyesOpen = list(
          Value = TRUE|FALSE,
          Confidence = 123.0
        ),
        MouthOpen = list(
          Value = TRUE|FALSE,
          Confidence = 123.0
        ),
        Emotions = list(
          list(
            Type = "HAPPY"|"SAD"|"ANGRY"|"CONFUSED"|"DISGUSTED"|"SURPRISED"|"CALM"|"UNKNOWN"|"FEAR",
            Confidence = 123.0
          )
        ),
        Landmarks = list(
          list(
            Type = "eyeLeft"|"eyeRight"|"nose"|"mouthLeft"|"mouthRight"|"leftEyeBrowLeft"|"leftEyeBrowRight"|"leftEyeBrowUp"|"rightEyeBrowLeft"|"rightEyeBrowRight"|"rightEyeBrowUp"|"leftEyeLeft"|"leftEyeRight"|"leftEyeUp"|"leftEyeDown"|"rightEyeLeft"|"rightEyeRight"|"rightEyeUp"|"rightEyeDown"|"noseLeft"|"noseRight"|"mouthUp"|"mouthDown"|"leftPupil"|"rightPupil"|"upperJawlineLeft"|"midJawlineLeft"|"chinBottom"|"midJawlineRight"|"upperJawlineRight",
            X = 123.0,
            Y = 123.0
          )
        ),
        Pose = list(
          Roll = 123.0,
          Yaw = 123.0,
          Pitch = 123.0
        ),
        Quality = list(
          Brightness = 123.0,
          Sharpness = 123.0
        ),
        Confidence = 123.0
      )
    )
  ),
  OrientationCorrection = "ROTATE_0"|"ROTATE_90"|"ROTATE_180"|"ROTATE_270",
  FaceModelVersion = "string",
  UnindexedFaces = list(
    list(
      Reasons = list(
        "EXCEEDS_MAX_FACES"|"EXTREME_POSE"|"LOW_BRIGHTNESS"|"LOW_SHARPNESS"|"LOW_CONFIDENCE"|"SMALL_BOUNDING_BOX"|"LOW_FACE_QUALITY"
      ),
      FaceDetail = list(
        BoundingBox = list(
          Width = 123.0,
          Height = 123.0,
          Left = 123.0,
          Top = 123.0
        ),
        AgeRange = list(
          Low = 123,
          High = 123
        ),
        Smile = list(
          Value = TRUE|FALSE,
          Confidence = 123.0
        ),
        Eyeglasses = list(
          Value = TRUE|FALSE,
          Confidence = 123.0
        ),
        Sunglasses = list(
          Value = TRUE|FALSE,
          Confidence = 123.0
        ),
        Gender = list(
          Value = "Male"|"Female",
          Confidence = 123.0
        ),
        Beard = list(
          Value = TRUE|FALSE,
          Confidence = 123.0
        ),
        Mustache = list(
          Value = TRUE|FALSE,
          Confidence = 123.0
        ),
        EyesOpen = list(
          Value = TRUE|FALSE,
          Confidence = 123.0
        ),
        MouthOpen = list(
          Value = TRUE|FALSE,
          Confidence = 123.0
        ),
        Emotions = list(
          list(
            Type = "HAPPY"|"SAD"|"ANGRY"|"CONFUSED"|"DISGUSTED"|"SURPRISED"|"CALM"|"UNKNOWN"|"FEAR",
            Confidence = 123.0
          )
        ),
        Landmarks = list(
          list(
            Type = "eyeLeft"|"eyeRight"|"nose"|"mouthLeft"|"mouthRight"|"leftEyeBrowLeft"|"leftEyeBrowRight"|"leftEyeBrowUp"|"rightEyeBrowLeft"|"rightEyeBrowRight"|"rightEyeBrowUp"|"leftEyeLeft"|"leftEyeRight"|"leftEyeUp"|"leftEyeDown"|"rightEyeLeft"|"rightEyeRight"|"rightEyeUp"|"rightEyeDown"|"noseLeft"|"noseRight"|"mouthUp"|"mouthDown"|"leftPupil"|"rightPupil"|"upperJawlineLeft"|"midJawlineLeft"|"chinBottom"|"midJawlineRight"|"upperJawlineRight",
            X = 123.0,
            Y = 123.0
          )
        ),
        Pose = list(
          Roll = 123.0,
          Yaw = 123.0,
          Pitch = 123.0
        ),
        Quality = list(
          Brightness = 123.0,
          Sharpness = 123.0
        ),
        Confidence = 123.0
      )
    )
  )
)

Request syntax

svc$index_faces(
  CollectionId = "string",
  Image = list(
    Bytes = raw,
    S3Object = list(
      Bucket = "string",
      Name = "string",
      Version = "string"
    )
  ),
  ExternalImageId = "string",
  DetectionAttributes = list(
    "DEFAULT"|"ALL"
  ),
  MaxFaces = 123,
  QualityFilter = "NONE"|"AUTO"|"LOW"|"MEDIUM"|"HIGH"
)

Examples

## Not run: 
# This operation detects faces in an image and adds them to the specified
# Rekognition collection.
svc$index_faces(
  CollectionId = "myphotos",
  DetectionAttributes = list(),
  ExternalImageId = "myphotoid",
  Image = list(
    S3Object = list(
      Bucket = "mybucket",
      Name = "myphoto"
    )
  )
)

## End(Not run)
paws.machine.learning

Amazon Web Services Machine Learning Services

v0.1.11
Apache License (>= 2.0)
Authors
David Kretch [aut, cre], Adam Banker [aut], Amazon.com, Inc. [cph]
Initial release

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