Adding faces to a collection - Amazon Rekognition

Adding faces to a collection

You can use the IndexFaces operation to detect faces in an image and add them to a collection. For each face detected, Amazon Rekognition extracts facial features and stores the feature information in a database. In addition, the command stores metadata for each face that's detected in the specified face collection. Amazon Rekognition doesn't store the actual image bytes.

For information about providing suitable faces for indexing, see Recommendations for facial comparison input images.

For each face, the IndexFaces operation persists the following information:

  • Multidimensional facial featuresIndexFaces uses facial analysis to extract multidimensional information about the facial features and stores the information in the face collection. You can't access this information directly. However, Amazon Rekognition uses this information when it searches a face collection for face matches.

     

  • Metadata – The metadata for each face includes a bounding box, confidence level (that the bounding box contains a face), IDs assigned by Amazon Rekognition (face ID and image ID), and an external image ID (if you provided it) in the request. This information is returned to you in response to the IndexFaces API call. For an example, see the face element in the following example response.

    The service returns this metadata in response to the following API calls:

     

The number of faces indexed by IndexFaces depends on the version of the face detection model that's associated with the input collection. For more information, see Model versioning.

Information about indexed faces is returned in an array of FaceRecord objects.

You might want to associate indexed faces with the image they were detected in. For example, you might want to maintain a client-side index of images and faces in the images. To associate faces with an image, specify an image ID in the ExternalImageId request parameter. The image ID can be the file name or another ID that you create.

In addition to the preceding information that the API persists in the face collection, the API also returns face details that aren't persisted in the collection. (See the faceDetail element in the following example response).

Note

DetectFaces returns the same information, so you don't need to call both DetectFaces and IndexFaces for the same image.

Filtering faces

The IndexFaces operation enables you to filter the faces that are indexed from an image. With IndexFaces you can specify a maximum number of faces to index, or you can choose to only index faces detected with a high quality.

You can specify the maximum number of faces that are indexed by IndexFaces by using 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 faces of people standing in the background.

By default, IndexFaces chooses a quality bar that's used to filter out faces. You can use the QualityFilter input parameter to explicitly set the quality bar. The values are:

  • AUTO — Amazon Rekognition chooses the quality bar that's used to filter out faces (default value).

  • LOW — All except the lowest quality faces are indexed.

  • MEDIUM

  • HIGH — Only the highest quality faces are indexed.

  • NONE - No faces are filtered out based on quality.

IndexFaces filters faces for the following reasons:

  • 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.

Note

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

Information about faces that aren't indexed by IndexFaces is returned in an array of UnindexedFace objects. The Reasons array contains a list of reasons why a face isn't indexed. For example, a value of EXCEEDS_MAX_FACES is a face that's not indexed because the number of faces specified by MaxFaces has already been detected.

For more information, see Managing faces in a collection.

To add faces to a collection (SDK)

  1. If you haven't already:

    1. Create or update an IAM user with AmazonRekognitionFullAccess and AmazonS3ReadOnlyAccess permissions. For more information, see Step 1: Set up an AWS account and create an IAM user.

    2. Install and configure the AWS CLI and the AWS SDKs. For more information, see Step 2: Set up the AWS CLI and AWS SDKs.

  2. Upload an image (containing one or more faces) to your Amazon S3 bucket.

    For instructions, see Uploading Objects into Amazon S3 in the Amazon Simple Storage Service User Guide.

  3. Use the following examples to call the IndexFaces operation.

    Java

    This example displays the face identifiers for faces added to the collection.

    Change the value of collectionId to the name of the collection that you want to add a face to. Replace the values of bucket and photo with the names of the Amazon S3 bucket and image that you used in step 2. The .withMaxFaces(1) parameter restricts the number of indexed faces to 1. Remove or change its value to suit your needs.

    //Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. //PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.) package aws.example.rekognition.image; import com.amazonaws.services.rekognition.AmazonRekognition; import com.amazonaws.services.rekognition.AmazonRekognitionClientBuilder; import com.amazonaws.services.rekognition.model.FaceRecord; import com.amazonaws.services.rekognition.model.Image; import com.amazonaws.services.rekognition.model.IndexFacesRequest; import com.amazonaws.services.rekognition.model.IndexFacesResult; import com.amazonaws.services.rekognition.model.QualityFilter; import com.amazonaws.services.rekognition.model.S3Object; import com.amazonaws.services.rekognition.model.UnindexedFace; import java.util.List; public class AddFacesToCollection { public static final String collectionId = "MyCollection"; public static final String bucket = "bucket"; public static final String photo = "input.jpg"; public static void main(String[] args) throws Exception { AmazonRekognition rekognitionClient = AmazonRekognitionClientBuilder.defaultClient(); Image image = new Image() .withS3Object(new S3Object() .withBucket(bucket) .withName(photo)); IndexFacesRequest indexFacesRequest = new IndexFacesRequest() .withImage(image) .withQualityFilter(QualityFilter.AUTO) .withMaxFaces(1) .withCollectionId(collectionId) .withExternalImageId(photo) .withDetectionAttributes("DEFAULT"); IndexFacesResult indexFacesResult = rekognitionClient.indexFaces(indexFacesRequest); System.out.println("Results for " + photo); System.out.println("Faces indexed:"); List<FaceRecord> faceRecords = indexFacesResult.getFaceRecords(); for (FaceRecord faceRecord : faceRecords) { System.out.println(" Face ID: " + faceRecord.getFace().getFaceId()); System.out.println(" Location:" + faceRecord.getFaceDetail().getBoundingBox().toString()); } List<UnindexedFace> unindexedFaces = indexFacesResult.getUnindexedFaces(); System.out.println("Faces not indexed:"); for (UnindexedFace unindexedFace : unindexedFaces) { System.out.println(" Location:" + unindexedFace.getFaceDetail().getBoundingBox().toString()); System.out.println(" Reasons:"); for (String reason : unindexedFace.getReasons()) { System.out.println(" " + reason); } } } }
    Java V2

    This code is taken from the AWS Documentation SDK examples GitHub repository. See the full example here.

    public static void addToCollection(RekognitionClient rekClient, String collectionId, String sourceImage) { try { InputStream sourceStream = new FileInputStream(sourceImage); SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream); Image souImage = Image.builder() .bytes(sourceBytes) .build(); IndexFacesRequest facesRequest = IndexFacesRequest.builder() .collectionId(collectionId) .image(souImage) .maxFaces(1) .qualityFilter(QualityFilter.AUTO) .detectionAttributes(Attribute.DEFAULT) .build(); IndexFacesResponse facesResponse = rekClient.indexFaces(facesRequest); // Display the results. System.out.println("Results for the image"); System.out.println("\n Faces indexed:"); List<FaceRecord> faceRecords = facesResponse.faceRecords(); for (FaceRecord faceRecord : faceRecords) { System.out.println(" Face ID: " + faceRecord.face().faceId()); System.out.println(" Location:" + faceRecord.faceDetail().boundingBox().toString()); } List<UnindexedFace> unindexedFaces = facesResponse.unindexedFaces(); System.out.println("Faces not indexed:"); for (UnindexedFace unindexedFace : unindexedFaces) { System.out.println(" Location:" + unindexedFace.faceDetail().boundingBox().toString()); System.out.println(" Reasons:"); for (Reason reason : unindexedFace.reasons()) { System.out.println("Reason: " + reason); } } } catch (RekognitionException | FileNotFoundException e) { System.out.println(e.getMessage()); System.exit(1); } }
    AWS CLI

    This AWS CLI command displays the JSON output for the index-faces CLI operation.

    Replace the value of collection-id with the name of the collection you want the face to be stored in. Replace the values of Bucket and Name with the Amazon S3 bucket and image file that you used in step 2. The max-faces parameter restricts the number of indexed faces to 1. Remove or change its value to suit your needs.

    aws rekognition index-faces \ --image '{"S3Object":{"Bucket":"bucket-name","Name":"file-name"}}' \ --collection-id "collection-id" \ --max-faces 1 \ --quality-filter "AUTO" \ --detection-attributes "ALL" \ --external-image-id "example-image.jpg"
    Python

    This example displays the face identifiers for faces added to the collection.

    Change the value of collectionId to the name of the collection that you want to add a face to. Replace the values of bucket and photo with the names of the Amazon S3 bucket and image that you used in step 2. The MaxFaces input parameter restricts the number of indexed faces to 1. Remove or change its value to suit your needs.

    #Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. #PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.) import boto3 def add_faces_to_collection(bucket,photo,collection_id): client=boto3.client('rekognition') response=client.index_faces(CollectionId=collection_id, Image={'S3Object':{'Bucket':bucket,'Name':photo}}, ExternalImageId=photo, MaxFaces=1, QualityFilter="AUTO", DetectionAttributes=['ALL']) print ('Results for ' + photo) print('Faces indexed:') for faceRecord in response['FaceRecords']: print(' Face ID: ' + faceRecord['Face']['FaceId']) print(' Location: {}'.format(faceRecord['Face']['BoundingBox'])) print('Faces not indexed:') for unindexedFace in response['UnindexedFaces']: print(' Location: {}'.format(unindexedFace['FaceDetail']['BoundingBox'])) print(' Reasons:') for reason in unindexedFace['Reasons']: print(' ' + reason) return len(response['FaceRecords']) def main(): bucket='bucket' collection_id='collection' photo='photo' indexed_faces_count=add_faces_to_collection(bucket, photo, collection_id) print("Faces indexed count: " + str(indexed_faces_count)) if __name__ == "__main__": main()
    .NET

    This example displays the face identifiers for faces added to the collection.

    Change the value of collectionId to the name of the collection that you want to add a face to. Replace the values of bucket and photo with the names of the Amazon S3 bucket and image that you used in step 2.

    //Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. //PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.) using System; using System.Collections.Generic; using Amazon.Rekognition; using Amazon.Rekognition.Model; public class AddFaces { public static void Example() { String collectionId = "MyCollection"; String bucket = "bucket"; String photo = "input.jpg"; AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient(); Image image = new Image() { S3Object = new S3Object() { Bucket = bucket, Name = photo } }; IndexFacesRequest indexFacesRequest = new IndexFacesRequest() { Image = image, CollectionId = collectionId, ExternalImageId = photo, DetectionAttributes = new List<String>(){ "ALL" } }; IndexFacesResponse indexFacesResponse = rekognitionClient.IndexFaces(indexFacesRequest); Console.WriteLine(photo + " added"); foreach (FaceRecord faceRecord in indexFacesResponse.FaceRecords) Console.WriteLine("Face detected: Faceid is " + faceRecord.Face.FaceId); } }

IndexFaces operation request

The input to IndexFaces is the image to be indexed and the collection to add the face or faces to.

{ "CollectionId": "MyCollection", "Image": { "S3Object": { "Bucket": "bucket", "Name": "input.jpg" } }, "ExternalImageId": "input.jpg", "DetectionAttributes": [ "DEFAULT" ], "MaxFaces": 1, "QualityFilter": "AUTO" }

IndexFaces operation response

IndexFaces returns information about the faces that were detected in the image. For example, the following JSON response includes the default detection attributes for faces detected in the input image. The example also shows faces not indexed because the value of the MaxFaces input parameter has been exceeded — the Reasons array contains EXCEEDS_MAX_FACES. If a face is not indexed for quality reasons, Reasons contains values such as LOW_SHARPNESS or LOW_BRIGHTNESS. For more information, see UnindexedFace .

{ "FaceModelVersion": "3.0", "FaceRecords": [ { "Face": { "BoundingBox": { "Height": 0.3247932195663452, "Left": 0.5055555701255798, "Top": 0.2743072211742401, "Width": 0.21444444358348846 }, "Confidence": 99.99998474121094, "ExternalImageId": "input.jpg", "FaceId": "b86e2392-9da1-459b-af68-49118dc16f87", "ImageId": "09f43d92-02b6-5cea-8fbd-9f187db2050d" }, "FaceDetail": { "BoundingBox": { "Height": 0.3247932195663452, "Left": 0.5055555701255798, "Top": 0.2743072211742401, "Width": 0.21444444358348846 }, "Confidence": 99.99998474121094, "Landmarks": [ { "Type": "eyeLeft", "X": 0.5751981735229492, "Y": 0.4010535478591919 }, { "Type": "eyeRight", "X": 0.6511467099189758, "Y": 0.4017036259174347 }, { "Type": "nose", "X": 0.6314528584480286, "Y": 0.4710812568664551 }, { "Type": "mouthLeft", "X": 0.5879443287849426, "Y": 0.5171778798103333 }, { "Type": "mouthRight", "X": 0.6444502472877502, "Y": 0.5164633989334106 } ], "Pose": { "Pitch": -10.313642501831055, "Roll": -1.0316886901855469, "Yaw": 18.079818725585938 }, "Quality": { "Brightness": 71.2919921875, "Sharpness": 78.74752044677734 } } } ], "OrientationCorrection": "", "UnindexedFaces": [ { "FaceDetail": { "BoundingBox": { "Height": 0.1329464465379715, "Left": 0.5611110925674438, "Top": 0.6832437515258789, "Width": 0.08777777850627899 }, "Confidence": 92.37225341796875, "Landmarks": [ { "Type": "eyeLeft", "X": 0.5796897411346436, "Y": 0.7452847957611084 }, { "Type": "eyeRight", "X": 0.6078574657440186, "Y": 0.742687463760376 }, { "Type": "nose", "X": 0.597953200340271, "Y": 0.7620673179626465 }, { "Type": "mouthLeft", "X": 0.5884202122688293, "Y": 0.7920381426811218 }, { "Type": "mouthRight", "X": 0.60627681016922, "Y": 0.7919750809669495 } ], "Pose": { "Pitch": 15.658954620361328, "Roll": -4.583454608917236, "Yaw": 10.558992385864258 }, "Quality": { "Brightness": 42.54612350463867, "Sharpness": 86.93206024169922 } }, "Reasons": [ "EXCEEDS_MAX_FACES" ] } ] }

To get all facial information, specify 'ALL' for the DetectionAttributes request parameter. For example, in the following example response, note the additional information in the faceDetail element, which isn't persisted on the server:

  • 25 facial landmarks (compared to only five in the preceding example)

  • Nine facial attributes (eyeglasses, beard, and so on)

  • Emotions (see the emotion element)

The face element provides metadata that's persisted on the server.

FaceModelVersion is the version of the face model that's associated with the collection. For more information, see Model versioning.

OrientationCorrection is the estimated orientation of the image. Orientation correction information is not returned if you are using a version of the face detection model that is greater than version 3. For more information, see Getting image orientation and bounding box coordinates.

{ "FaceModelVersion": "3.0", "FaceRecords": [ { "Face": { "BoundingBox": { "Height": 0.06333333253860474, "Left": 0.17185185849666595, "Top": 0.7366666793823242, "Width": 0.11061728745698929 }, "Confidence": 99.99999237060547, "ExternalImageId": "input.jpg", "FaceId": "578e2e1b-d0b0-493c-aa39-ba476a421a34", "ImageId": "9ba38e68-35b6-5509-9d2e-fcffa75d1653" }, "FaceDetail": { "AgeRange": { "High": 25, "Low": 15 }, "Beard": { "Confidence": 99.98077392578125, "Value": false }, "BoundingBox": { "Height": 0.06333333253860474, "Left": 0.17185185849666595, "Top": 0.7366666793823242, "Width": 0.11061728745698929 }, "Confidence": 99.99999237060547, "Emotions": [ { "Confidence": 95.40877532958984, "Type": "HAPPY" }, { "Confidence": 6.6088080406188965, "Type": "CALM" }, { "Confidence": 0.7385611534118652, "Type": "SAD" } ], "Eyeglasses": { "Confidence": 99.96795654296875, "Value": false }, "EyesOpen": { "Confidence": 64.0671157836914, "Value": true }, "Gender": { "Confidence": 100, "Value": "Female" }, "Landmarks": [ { "Type": "eyeLeft", "X": 0.21361233294010162, "Y": 0.757106363773346 }, { "Type": "eyeRight", "X": 0.2518567442893982, "Y": 0.7599404454231262 }, { "Type": "nose", "X": 0.2262365221977234, "Y": 0.7711842060089111 }, { "Type": "mouthLeft", "X": 0.2050037682056427, "Y": 0.7801263332366943 }, { "Type": "mouthRight", "X": 0.2430567592382431, "Y": 0.7836716771125793 }, { "Type": "leftPupil", "X": 0.2161938101053238, "Y": 0.756662905216217 }, { "Type": "rightPupil", "X": 0.2523181438446045, "Y": 0.7603650689125061 }, { "Type": "leftEyeBrowLeft", "X": 0.20066319406032562, "Y": 0.7501518130302429 }, { "Type": "leftEyeBrowUp", "X": 0.2130996286869049, "Y": 0.7480520606040955 }, { "Type": "leftEyeBrowRight", "X": 0.22584207355976105, "Y": 0.7504606246948242 }, { "Type": "rightEyeBrowLeft", "X": 0.24509544670581818, "Y": 0.7526801824569702 }, { "Type": "rightEyeBrowUp", "X": 0.2582615911960602, "Y": 0.7516844868659973 }, { "Type": "rightEyeBrowRight", "X": 0.26881539821624756, "Y": 0.7554477453231812 }, { "Type": "leftEyeLeft", "X": 0.20624476671218872, "Y": 0.7568746209144592 }, { "Type": "leftEyeRight", "X": 0.22105035185813904, "Y": 0.7582521438598633 }, { "Type": "leftEyeUp", "X": 0.21401576697826385, "Y": 0.7553104162216187 }, { "Type": "leftEyeDown", "X": 0.21317370235919952, "Y": 0.7584449648857117 }, { "Type": "rightEyeLeft", "X": 0.24393919110298157, "Y": 0.7600628137588501 }, { "Type": "rightEyeRight", "X": 0.2598416209220886, "Y": 0.7605880498886108 }, { "Type": "rightEyeUp", "X": 0.2519053518772125, "Y": 0.7582084536552429 }, { "Type": "rightEyeDown", "X": 0.25177454948425293, "Y": 0.7612871527671814 }, { "Type": "noseLeft", "X": 0.2185886949300766, "Y": 0.774715781211853 }, { "Type": "noseRight", "X": 0.23328955471515656, "Y": 0.7759330868721008 }, { "Type": "mouthUp", "X": 0.22446128726005554, "Y": 0.7805567383766174 }, { "Type": "mouthDown", "X": 0.22087252140045166, "Y": 0.7891407608985901 } ], "MouthOpen": { "Confidence": 95.87068939208984, "Value": false }, "Mustache": { "Confidence": 99.9828109741211, "Value": false }, "Pose": { "Pitch": -0.9409101605415344, "Roll": 7.233824253082275, "Yaw": -2.3602254390716553 }, "Quality": { "Brightness": 32.01998519897461, "Sharpness": 93.67259216308594 }, "Smile": { "Confidence": 86.7142105102539, "Value": true }, "Sunglasses": { "Confidence": 97.38925170898438, "Value": false } } } ], "OrientationCorrection": "ROTATE_0" "UnindexedFaces": [] }