本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。
# 新增人臉到集合
您可以使用 [IndexFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_IndexFaces.html) 操作來偵測映像中的人臉，並將其新增至集合。對於每個偵測到的人臉，Amazon Rekognition 將擷取人臉特徵並將特徵資訊存放於資料庫中。此外，該命令將為每個在指定人臉集合中偵測到的人臉儲存中繼資料。Amazon Rekognition 不會儲存實際的映像位元組。
如需為編制索引提供適合臉部的相關資訊，請參閱 [人臉比較輸入映像的建議](recommendations-facial-input-images.md)。
`IndexFaces` 操作將保留每個人臉的下列資訊：
+ **多維度人臉特徵**：`IndexFaces` 使用人臉分析來擷取人臉特徵的多維度資訊，並將資訊儲存於人臉集合中。您無法直接存取此資訊。不過，Amazon Rekognition 會在搜尋人臉集合並比對人臉時使用此資訊。
+ **中繼資料**：每個人臉的中繼資料包括要求的週框方塊、可信度等級 (週框方塊標註的人臉)、由 Amazon Rekognition 指定的 ID (人臉 ID 與映像 ID)、以及請求中的外部映像 ID (若您提供此資訊)。此資訊會在對於 `IndexFaces` API 呼叫的回應中回傳給您。如需範例，請參閱下列範例回應中的 `face` 元素。
此服務會傳回此中繼資料，以回應下列 API 呼叫：
+ `[ListFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListFaces.html)`
+ 搜尋人臉操作：對於 [SearchFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchFaces.html) 與 [SearchFacesByImage](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchFacesByImage.html) 的回應將傳回對每個相符人臉的配對可信度，同時提供相符人臉之中繼資料。
由 `IndexFaces` 索引的臉部數量取決於與輸入集合相關聯的臉部偵測模型的版本。如需詳細資訊，請參閱 [了解模型版本控制](face-detection-model.md)。
有索引之人臉的相關資訊會以 [FaceRecord](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_FaceRecord.html) 物件陣列傳回。
您可能想要將編入索引的人臉與偵測到這些人臉的映像連結。例如，您可能想要維護映像的使用者端索引以及映像中的人臉。若要將人臉與映像連結，請在 `ExternalImageId` 請求參數中指定一個映像 ID。映像 ID 可以是檔案名稱或建立的另一個 ID。
除了上述關於 API 保留於人臉集合中的資訊外，API 也會傳回未保留於集合中的人臉詳細資訊。(請參閱下列範例回應中的 `faceDetail` 元素)。
**注意**
`DetectFaces` 將傳回相同的資訊，因此您不需要同時呼叫 `DetectFaces` 和 `IndexFaces`。
## 篩選人臉
這個 IndexFaces 操作可讓您篩選根據映像建立索引的人臉。運用 `IndexFaces`，您可以指定索引建立時所要使用的最多人臉數目，或者選擇只對高品質偵測人臉建立索引。
使用 `MaxFaces` 輸入參數，您可以指定 `IndexFaces` 在建立索引時所用的最多人臉數目。若您在建立索引時想要運用映像中尺寸最大的人臉，而不要使用尺寸最小的人臉時，例如，背景中站立人群的人臉，就能使用這項參數。
在預設情況下，`IndexFaces` 選擇用於篩選出臉部的品質列。您可以使用 `QualityFilter` 輸入參數，以明確設定品質列。這些值為：
+ `AUTO`：Amazon Rekognition 選擇並不用於篩選出人臉的品質列 (預設值)。
+ `LOW`：除了品質最低的人臉之外，所有人臉都會加以索引。
+ `MEDIUM`
+ `HIGH`：僅對品質最高的人臉行索引。
+ `NONE`：不會根據品質來篩選掉任何人臉。
`IndexFaces` 篩選人臉的原因如下：
+ 相較於映像尺寸，人臉過小。
+ 臉部過於模糊。
+ 映像過暗。
+ 臉部表情過於誇張。
+ 臉部沒有足夠的細節，不適合臉部搜尋。
**注意**
若要使用品質篩選，您必須使用臉部模型第 3 版或更高版本的相關聯集合。若要取得與集合有關聯之人臉模型的版本，請呼叫 [DescribeCollection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeCollection.html)。
未經 `IndexFaces` 建立索引之人臉的相關資訊，將透過 [UnindexedFace](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_UnindexedFace.html) 物件陣列傳回。`Reasons` 陣列包含為何無法為人臉建立索引的原因清單。例如，當值為 `EXCEEDS_MAX_FACES` 時，表示因為已偵測到透過 `MaxFaces` 指定的人臉數目，所以無法為人臉建立索引。
如需詳細資訊，請參閱 [管理集合中的人臉](managing-face-collections.md#collections-index-faces)。
**若要新增臉部到集合 (SDK)**
1. 如果您尚未執行：
1. 建立或更新具有 `AmazonRekognitionFullAccess` 和 `AmazonS3ReadOnlyAccess` 許可的使用者。如需詳細資訊，請參閱[步驟 1：設定 AWS 帳戶並建立使用者](setting-up.md#setting-up-iam)。
1. 安裝和設定 AWS CLI 和 AWS SDKs。如需詳細資訊，請參閱[步驟 2：設定 AWS CLI 和 AWS SDKs](setup-awscli-sdk.md)。
1. 將映像 (含有一個或多個人臉) 上傳至您的 Amazon S3 儲存貯體。
如需指示說明，請參閱《Amazon Simple Storage Service 使用者指南》**中的[上傳物件至 Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UploadingObjectsintoAmazonS3.html)。
1. 使用下列範例來呼叫 `IndexFaces` 操作。
------
#### [ Java ]
此範例顯示新增到集合的人臉的人臉識別符。
將 `collectionId` 的值變更為要新增到人臉的集合的名稱。將 `bucket` 與 `photo` 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。`.withMaxFaces(1)` 參數會將用於建立索引的人臉數目限制為 1。移除或變更其值以符合您的需求。
```
//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 faceRecords = indexFacesResult.getFaceRecords();
for (FaceRecord faceRecord : faceRecords) {
System.out.println(" Face ID: " + faceRecord.getFace().getFaceId());
System.out.println(" Location:" + faceRecord.getFaceDetail().getBoundingBox().toString());
}
List 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 ]
此程式碼取自 AWS 文件開發套件範例 GitHub 儲存庫。請參閱[此處](https://github.com/awsdocs/aws-doc-sdk-examples/blob/master/javav2/example_code/rekognition/src/main/java/com/example/rekognition/AddFacesToCollection.java)的完整範例。
```
//snippet-start:[rekognition.java2.add_faces_collection.import]
import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.IndexFacesResponse;
import software.amazon.awssdk.services.rekognition.model.IndexFacesRequest;
import software.amazon.awssdk.services.rekognition.model.Image;
import software.amazon.awssdk.services.rekognition.model.QualityFilter;
import software.amazon.awssdk.services.rekognition.model.Attribute;
import software.amazon.awssdk.services.rekognition.model.FaceRecord;
import software.amazon.awssdk.services.rekognition.model.UnindexedFace;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.Reason;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.InputStream;
import java.util.List;
//snippet-end:[rekognition.java2.add_faces_collection.import]
/**
* Before running this Java V2 code example, set up your development environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class AddFacesToCollection {
public static void main(String[] args) {
final String usage = "\n" +
"Usage: " +
" \n\n" +
"Where:\n" +
" collectionName - The name of the collection.\n" +
" sourceImage - The path to the image (for example, C:\\AWS\\pic1.png). \n\n";
if (args.length != 2) {
System.out.println(usage);
System.exit(1);
}
String collectionId = args[0];
String sourceImage = args[1];
Region region = Region.US_EAST_1;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
.build();
addToCollection(rekClient, collectionId, sourceImage);
rekClient.close();
}
// snippet-start:[rekognition.java2.add_faces_collection.main]
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);
System.out.println("Results for the image");
System.out.println("\n Faces indexed:");
List faceRecords = facesResponse.faceRecords();
for (FaceRecord faceRecord : faceRecords) {
System.out.println(" Face ID: " + faceRecord.face().faceId());
System.out.println(" Location:" + faceRecord.faceDetail().boundingBox().toString());
}
List 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);
}
}
// snippet-end:[rekognition.java2.add_faces_collection.main]
}
```
------
#### [ AWS CLI ]
此 AWS CLI 命令會顯示 CLI 操作的 JSON `index-faces` 輸出。
以您要儲存臉部的集合之名稱取代 `collection-id` 的值。將 `Bucket` 與 `Name` 的值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像檔案名稱。`max-faces` 參數會將用於建立索引的人臉數目限制為 1。移除或變更其值以符合您的需求。將建立 Rekognition 工作階段的行中 `profile_name` 值取代為您開發人員設定檔的名稱。
```
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" --profile profile-name
```
如果您在 Windows 裝置上存取 CLI，請使用雙引號而非單引號，並以反斜線 (即\$1) 替代內部雙引號，以解決您可能遇到的任何剖析器錯誤。例如，請參閱下列內容：
```
aws rekognition index-faces --image "{\"S3Object\":{\"Bucket\":\"bucket-name\",\"Name\":\"image-name\"}}" \
--collection-id "collection-id" --max-faces 1 --quality-filter "AUTO" --detection-attributes "ALL" \
--external-image-id "example-image.jpg" --profile profile-name
```
------
#### [ Python ]
此範例顯示新增到集合的人臉的人臉識別符。
將 `collectionId` 的值變更為要新增到人臉的集合的名稱。將 `bucket` 與 `photo` 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。`MaxFaces` 輸入參數會將用於建立索引的人臉數目限制為 1。移除或變更其值以符合您的需求。將建立 Rekognition 工作階段的行中 `profile_name` 值取代為您開發人員設定檔的名稱。
```
# 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):
session = boto3.Session(profile_name='profile-name')
client = session.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 = 'amzn-s3-demo-bucket'
collection_id = 'collection-id'
photo = 'photo-name'
indexed_faces_count = add_faces_to_collection(bucket, photo, collection_id)
print("Faces indexed count: " + str(indexed_faces_count))
if __name__ == "__main__":
main()
```
------
#### [ .NET ]
此範例顯示新增到集合的人臉的人臉識別符。
將 `collectionId` 的值變更為要新增到人臉的集合的名稱。將 `bucket` 與 `photo` 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。
```
//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 = "amzn-s3-demo-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(){ "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 操作要求
`IndexFaces` 的輸入是要索引的映像和新增人臉或人臉的集合。
```
{
"CollectionId": "MyCollection",
"Image": {
"S3Object": {
"Bucket": "bucket",
"Name": "input.jpg"
}
},
"ExternalImageId": "input.jpg",
"DetectionAttributes": [
"DEFAULT"
],
"MaxFaces": 1,
"QualityFilter": "AUTO"
}
```
## IndexFaces 操作回應
`IndexFaces` 傳回有關在映像中偵測到的人臉的資訊。例如，以下 JSON 回應包含輸入映像中偵測到的人臉的預設偵測屬性。範例當中還會示範已經超過 `MaxFaces` 輸入參數的值 ( `Reasons` 陣列包含 * EXCEEDS\$1MAX\$1FACES*)，導致無法為人臉建立索引。當 `Reasons` 包含像是 *LOW\$1SHARPNESS* 或 *LOW\$1BRIGHTNESS* 等品質原因時，導致無法為人臉建立索引。如需詳細資訊，請參閱 [UnindexedFace](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_UnindexedFace.html)。
```
{
"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"
]
}
]
}
```
若要取得所有臉部資訊，請指定 `DetectionAttributes` 請求參數為『ALL』。例如，在以下範例回應中可找到 `faceDetail` 元素中的其它資訊，而這些資訊不會儲存在伺服器上：
+ 25 個臉部特徵點 (相較於上述範例中只有 5 個特徵點)
+ 十個面部屬性 (眼鏡、鬍鬚、人臉遮挡、眼睛凝視方向等)
+ 表情 (請參閱 `emotion` 元素)
`face` 元素提供保留在伺服器上的中繼資料。
`FaceModelVersion` 是與集合相關聯的臉部模型版本。如需詳細資訊，請參閱 [了解模型版本控制](face-detection-model.md)。
`OrientationCorrection` 是映像的估計方向。如果您使用的臉部偵測模型版本大於第 3 版，則不會傳回方向更正資訊。如需詳細資訊，請參閱 [取得映像方向與週框方塊座標](images-orientation.md)。
下列範例回應顯示指定 ["ALL"] 時傳回的 JSON：
```
{
"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"
}
],
"EyeDirection": {
"yaw": 16.299732,
"pitch": -6.407457,
"confidence": 99.968704
}
"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": []
}
```