Compare Faces
| rekognition_compare_faces | R Documentation |
Compares a face in the source input image with each of the 100 largest faces detected in the target input image¶
Description¶
Compares a face in the source input image with each of the 100 largest faces detected in the target input image.
If the source image contains multiple faces, the service detects the largest face and compares it with each face detected in the target image.
CompareFaces uses machine learning algorithms, which are probabilistic.
A false negative is an incorrect prediction that a face in the target
image has a low similarity confidence score when compared to the face in
the source image. To reduce the probability of false negatives, we
recommend that you compare the target image against multiple source
images. If you plan to use compare_faces to make a decision that
impacts an individual's rights, privacy, or access to services, we
recommend that you pass the result to a human for review and further
validation before taking action.
You pass the input and target images either as base64-encoded image bytes or as references to images 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.
In response, the operation returns an array of face matches ordered by similarity score in descending order. For each face match, the response provides a bounding box of the face, facial landmarks, pose details (pitch, roll, and yaw), quality (brightness and sharpness), and confidence value (indicating the level of confidence that the bounding box contains a face). The response also provides a similarity score, which indicates how closely the faces match.
By default, only faces with a similarity score of greater than or equal
to 80% are returned in the response. You can change this value by
specifying the SimilarityThreshold parameter.
compare_faces also returns an array of faces that don't match the
source image. For each face, it returns a bounding box, confidence
value, landmarks, pose details, and quality. The response also returns
information about the face in the source image, including the bounding
box of the face and confidence value.
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. Use QualityFilter to set the quality
bar by specifying LOW, MEDIUM, or HIGH. If you do not want to
filter detected faces, specify NONE. The default value is NONE.
If the image doesn't contain Exif metadata, compare_faces returns
orientation information for the source and target images. Use these
values to display the images with the correct image orientation.
If no faces are detected in the source or target images, compare_faces
returns an InvalidParameterException error.
This is a stateless API operation. That is, data returned by this operation doesn't persist.
For an example, see Comparing Faces in Images in the Amazon Rekognition Developer Guide.
This operation requires permissions to perform the
rekognition:CompareFaces action.
Usage¶
rekognition_compare_faces(SourceImage, TargetImage, SimilarityThreshold,
QualityFilter)
Arguments¶
SourceImage |
[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 is not supported. If you are using an AWS SDK to call Amazon Rekognition, you might not
need to base64-encode image bytes passed using the |
TargetImage |
[required] The target 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 is not supported. If you are using an AWS SDK to call Amazon Rekognition, you might not
need to base64-encode image bytes passed using the |
SimilarityThreshold |
The minimum level of confidence in the face matches that a match
must meet to be included in the |
QualityFilter |
A filter that specifies a quality bar for how much filtering is
done to identify faces. Filtered faces aren't compared. If you specify
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(
SourceImageFace = list(
BoundingBox = list(
Width = 123.0,
Height = 123.0,
Left = 123.0,
Top = 123.0
),
Confidence = 123.0
),
FaceMatches = list(
list(
Similarity = 123.0,
Face = list(
BoundingBox = list(
Width = 123.0,
Height = 123.0,
Left = 123.0,
Top = 123.0
),
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
),
Emotions = list(
list(
Type = "HAPPY"|"SAD"|"ANGRY"|"CONFUSED"|"DISGUSTED"|"SURPRISED"|"CALM"|"UNKNOWN"|"FEAR",
Confidence = 123.0
)
),
Smile = list(
Value = TRUE|FALSE,
Confidence = 123.0
)
)
)
),
UnmatchedFaces = list(
list(
BoundingBox = list(
Width = 123.0,
Height = 123.0,
Left = 123.0,
Top = 123.0
),
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
),
Emotions = list(
list(
Type = "HAPPY"|"SAD"|"ANGRY"|"CONFUSED"|"DISGUSTED"|"SURPRISED"|"CALM"|"UNKNOWN"|"FEAR",
Confidence = 123.0
)
),
Smile = list(
Value = TRUE|FALSE,
Confidence = 123.0
)
)
),
SourceImageOrientationCorrection = "ROTATE_0"|"ROTATE_90"|"ROTATE_180"|"ROTATE_270",
TargetImageOrientationCorrection = "ROTATE_0"|"ROTATE_90"|"ROTATE_180"|"ROTATE_270"
)
Request syntax¶
svc$compare_faces(
SourceImage = list(
Bytes = raw,
S3Object = list(
Bucket = "string",
Name = "string",
Version = "string"
)
),
TargetImage = list(
Bytes = raw,
S3Object = list(
Bucket = "string",
Name = "string",
Version = "string"
)
),
SimilarityThreshold = 123.0,
QualityFilter = "NONE"|"AUTO"|"LOW"|"MEDIUM"|"HIGH"
)
Examples¶
## Not run:
# This operation compares the largest face detected in the source image
# with each face detected in the target image.
svc$compare_faces(
SimilarityThreshold = 90L,
SourceImage = list(
S3Object = list(
Bucket = "mybucket",
Name = "mysourceimage"
)
),
TargetImage = list(
S3Object = list(
Bucket = "mybucket",
Name = "mytargetimage"
)
)
)
## End(Not run)