# Detecting faces in a stored video
<a name="faces-sqs-video"></a>

Amazon Rekognition Video can detect faces in videos that are stored in an Amazon S3 bucket and provide information such as: 
+ The time or times faces are detected in a video.
+ The location of faces in the video frame at the time they were detected.
+ Facial landmarks such as the position of the left eye. 
+ Additional attributes as explained on the [Guidelines on face attributes](guidance-face-attributes.md) page.

Amazon Rekognition Video face detection in stored videos is an asynchronous operation. To start the detection of faces in videos, call [StartFaceDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartFaceDetection.html). Amazon Rekognition Video publishes the completion status of the video analysis to an Amazon Simple Notification Service (Amazon SNS) topic. If the video analysis is successful, you can call [GetFaceDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetFaceDetection.html) to get the results of the video analysis. For more information about starting video analysis and getting the results, see [Calling Amazon Rekognition Video operations](api-video.md). 

This procedure expands on the code in [Analyzing a video stored in an Amazon S3 bucket with Java or Python (SDK)](video-analyzing-with-sqs.md), which uses an Amazon Simple Queue Service (Amazon SQS) queue to get the completion status of a video analysis request. 

**To detect faces in a video stored in an Amazon S3 bucket (SDK)**

1. Perform [Analyzing a video stored in an Amazon S3 bucket with Java or Python (SDK)](video-analyzing-with-sqs.md).

1. Add the following code to the class `VideoDetect` that you created in step 1.

------
#### [ AWS CLI ]
   + In the following code sample, change `amzn-s3-demo-bucket` and `video-name` to the Amazon S3 bucket name and file name that you specified in step 2.
   + Change `region-name` to the AWS region that you're using. Replace the value of `profile_name` with the name of your developer profile.
   + Change `TopicARN` to the ARN of the Amazon SNS topic you created in step 3 of [Configuring Amazon Rekognition Video](api-video-roles.md).
   + Change `RoleARN` to the ARN of the IAM service role you created in step 7 of [Configuring Amazon Rekognition Video](api-video-roles.md).

   ```
   aws rekognition start-face-detection --video "{"S3Object":{"Bucket":"amzn-s3-demo-bucket","Name":"Video-Name"}}" --notification-channel
   "{"SNSTopicArn":"Topic-ARN","RoleArn":"Role-ARN"}" --region region-name --profile profile-name
   ```

   If you are accessing the CLI on a Windows device, use double quotes instead of single quotes and escape the inner double quotes by backslash (i.e. \$1) to address any parser errors you may encounter. For an example, see the following: 

   ```
   aws rekognition start-face-detection --video "{\"S3Object\":{\"Bucket\":\"amzn-s3-demo-bucket\",\"Name\":\"Video-Name\"}}" --notification-channel
   "{\"SNSTopicArn\":\"Topic-ARN\",\"RoleArn\":\"Role-ARN\"}" --region region-name --profile profile-name
   ```

   After running the `StartFaceDetection` operation and getting the job ID number, run the following `GetFaceDetection` operation and provide the job ID number:

   ```
   aws rekognition get-face-detection --job-id job-id-number  --profile profile-name
   ```

------
#### [ Java ]

   ```
   //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.)
   
   
   private static void StartFaceDetection(String bucket, String video) throws Exception{
            
       NotificationChannel channel= new NotificationChannel()
               .withSNSTopicArn(snsTopicArn)
               .withRoleArn(roleArn);
       
       StartFaceDetectionRequest req = new StartFaceDetectionRequest()
               .withVideo(new Video()
                       .withS3Object(new S3Object()
                           .withBucket(bucket)
                           .withName(video)))
               .withNotificationChannel(channel);
                           
                           
       
       StartFaceDetectionResult startLabelDetectionResult = rek.startFaceDetection(req);
       startJobId=startLabelDetectionResult.getJobId();
       
   } 
   
   private static void GetFaceDetectionResults() throws Exception{
       
       int maxResults=10;
       String paginationToken=null;
       GetFaceDetectionResult faceDetectionResult=null;
       
       do{
           if (faceDetectionResult !=null){
               paginationToken = faceDetectionResult.getNextToken();
           }
       
           faceDetectionResult = rek.getFaceDetection(new GetFaceDetectionRequest()
                .withJobId(startJobId)
                .withNextToken(paginationToken)
                .withMaxResults(maxResults));
       
           VideoMetadata videoMetaData=faceDetectionResult.getVideoMetadata();
               
           System.out.println("Format: " + videoMetaData.getFormat());
           System.out.println("Codec: " + videoMetaData.getCodec());
           System.out.println("Duration: " + videoMetaData.getDurationMillis());
           System.out.println("FrameRate: " + videoMetaData.getFrameRate());
               
               
           //Show faces, confidence and detection times
           List<FaceDetection> faces= faceDetectionResult.getFaces();
        
           for (FaceDetection face: faces) { 
               long seconds=face.getTimestamp()/1000;
               System.out.print("Sec: " + Long.toString(seconds) + " ");
               System.out.println(face.getFace().toString());
               System.out.println();           
           }
       } while (faceDetectionResult !=null && faceDetectionResult.getNextToken() != null);
         
           
   }
   ```

   In the function `main`, replace the lines: 

   ```
           StartLabelDetection(amzn-s3-demo-bucket, video);
   
           if (GetSQSMessageSuccess()==true)
           	GetLabelDetectionResults();
   ```

   with:

   ```
           StartFaceDetection(amzn-s3-demo-bucket, video);
   
           if (GetSQSMessageSuccess()==true)
           	GetFaceDetectionResults();
   ```

------
#### [ Java V2 ]

   This code is taken from the AWS Documentation SDK examples GitHub repository. See the full example [here](https://github.com/awsdocs/aws-doc-sdk-examples/blob/master/javav2/example_code/rekognition/src/main/java/com/example/rekognition/VideoDetectFaces.java).

   ```
   //snippet-start:[rekognition.java2.recognize_video_faces.import]
   import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
   import software.amazon.awssdk.regions.Region;
   import software.amazon.awssdk.services.rekognition.RekognitionClient;
   import software.amazon.awssdk.services.rekognition.model.*;
   import java.util.List;
   //snippet-end:[rekognition.java2.recognize_video_faces.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 VideoDetectFaces {
   
    private static String startJobId ="";
    public static void main(String[] args) {
   
        final String usage = "\n" +
            "Usage: " +
            "   <bucket> <video> <topicArn> <roleArn>\n\n" +
            "Where:\n" +
            "   bucket - The name of the bucket in which the video is located (for example, (for example, amzn-s3-demo-bucket). \n\n"+
            "   video - The name of video (for example, people.mp4). \n\n" +
            "   topicArn - The ARN of the Amazon Simple Notification Service (Amazon SNS) topic. \n\n" +
            "   roleArn - The ARN of the AWS Identity and Access Management (IAM) role to use. \n\n" ;
   
        if (args.length != 4) {
            System.out.println(usage);
            System.exit(1);
        }
   
        String bucket = args[0];
        String video = args[1];
        String topicArn = args[2];
        String roleArn = args[3];
   
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
            .region(region)
            .credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
            .build();
   
        NotificationChannel channel = NotificationChannel.builder()
            .snsTopicArn(topicArn)
            .roleArn(roleArn)
            .build();
   
        StartFaceDetection(rekClient, channel, bucket, video);
        GetFaceResults(rekClient);
        System.out.println("This example is done!");
        rekClient.close();
    }
   
    // snippet-start:[rekognition.java2.recognize_video_faces.main]
    public static void StartFaceDetection(RekognitionClient rekClient,
                                          NotificationChannel channel,
                                          String bucket,
                                          String video) {
   
        try {
            S3Object s3Obj = S3Object.builder()
                .bucket(bucket)
                .name(video)
                .build();
   
            Video vidOb = Video.builder()
                .s3Object(s3Obj)
                .build();
   
            StartFaceDetectionRequest  faceDetectionRequest = StartFaceDetectionRequest.builder()
                .jobTag("Faces")
                .faceAttributes(FaceAttributes.ALL)
                .notificationChannel(channel)
                .video(vidOb)
                .build();
   
            StartFaceDetectionResponse startLabelDetectionResult = rekClient.startFaceDetection(faceDetectionRequest);
            startJobId=startLabelDetectionResult.jobId();
   
        } catch(RekognitionException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
   
    public static void GetFaceResults(RekognitionClient rekClient) {
   
        try {
            String paginationToken=null;
            GetFaceDetectionResponse faceDetectionResponse=null;
            boolean finished = false;
            String status;
            int yy=0 ;
   
            do{
                if (faceDetectionResponse !=null)
                    paginationToken = faceDetectionResponse.nextToken();
   
                GetFaceDetectionRequest recognitionRequest = GetFaceDetectionRequest.builder()
                    .jobId(startJobId)
                    .nextToken(paginationToken)
                    .maxResults(10)
                    .build();
   
                // Wait until the job succeeds
                while (!finished) {
   
                    faceDetectionResponse = rekClient.getFaceDetection(recognitionRequest);
                    status = faceDetectionResponse.jobStatusAsString();
   
                    if (status.compareTo("SUCCEEDED") == 0)
                        finished = true;
                    else {
                        System.out.println(yy + " status is: " + status);
                        Thread.sleep(1000);
                    }
                    yy++;
                }
   
                finished = false;
   
                // Proceed when the job is done - otherwise VideoMetadata is null
                VideoMetadata videoMetaData=faceDetectionResponse.videoMetadata();
                System.out.println("Format: " + videoMetaData.format());
                System.out.println("Codec: " + videoMetaData.codec());
                System.out.println("Duration: " + videoMetaData.durationMillis());
                System.out.println("FrameRate: " + videoMetaData.frameRate());
                System.out.println("Job");
   
                // Show face information
                List<FaceDetection> faces= faceDetectionResponse.faces();
   
                for (FaceDetection face: faces) {
                    String age = face.face().ageRange().toString();
                    String smile = face.face().smile().toString();
                    System.out.println("The detected face is estimated to be"
                                + age + " years old.");
                    System.out.println("There is a smile : "+smile);
                }
   
            } while (faceDetectionResponse !=null && faceDetectionResponse.nextToken() != null);
   
        } catch(RekognitionException | InterruptedException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
    // snippet-end:[rekognition.java2.recognize_video_faces.main]
   }
   ```

------
#### [ Python ]

   ```
   #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.)
   
       # ============== Faces===============
       def StartFaceDetection(self):
           response=self.rek.start_face_detection(Video={'S3Object': {'Bucket': self.bucket, 'Name': self.video}},
               NotificationChannel={'RoleArn': self.roleArn, 'SNSTopicArn': self.snsTopicArn})
   
           self.startJobId=response['JobId']
           print('Start Job Id: ' + self.startJobId)
   
       def GetFaceDetectionResults(self):
           maxResults = 10
           paginationToken = ''
           finished = False
   
           while finished == False:
               response = self.rek.get_face_detection(JobId=self.startJobId,
                                               MaxResults=maxResults,
                                               NextToken=paginationToken)
   
               print('Codec: ' + response['VideoMetadata']['Codec'])
               print('Duration: ' + str(response['VideoMetadata']['DurationMillis']))
               print('Format: ' + response['VideoMetadata']['Format'])
               print('Frame rate: ' + str(response['VideoMetadata']['FrameRate']))
               print()
   
               for faceDetection in response['Faces']:
                   print('Face: ' + str(faceDetection['Face']))
                   print('Confidence: ' + str(faceDetection['Face']['Confidence']))
                   print('Timestamp: ' + str(faceDetection['Timestamp']))
                   print()
   
               if 'NextToken' in response:
                   paginationToken = response['NextToken']
               else:
                   finished = True
   ```

   In the function `main`, replace the lines: 

   ```
       analyzer.StartLabelDetection()
       if analyzer.GetSQSMessageSuccess()==True:
           analyzer.GetLabelDetectionResults()
   ```

   with:

   ```
       analyzer.StartFaceDetection()
       if analyzer.GetSQSMessageSuccess()==True:
           analyzer.GetFaceDetectionResults()
   ```

------
**Note**  
If you've already run a video example other than [Analyzing a video stored in an Amazon S3 bucket with Java or Python (SDK)](video-analyzing-with-sqs.md), the function name to replace is different.

1. Run the code. Information about the faces that were detected in the video is shown.

## GetFaceDetection operation response
<a name="getfacedetection-operation-response"></a>

`GetFaceDetection` returns an array (`Faces`) that contains information about the faces detected in the video. An array element, [FaceDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_FaceDetection.html), exists for each time a face is detected in the video. The array elements returned are sorted by time, in milliseconds since the start of the video. 

The following example is a partial JSON response from `GetFaceDetection`. In the response, note the following:
+ **Bounding box** – The coordinates of the bounding box that surrounds the face.
+ **Confidence** – The level of confidence that the bounding box contains a face.
+ **Facial landmarks** – An array of facial landmarks. For each landmark (such as the left eye, right eye, and mouth), the response provides the `x` and `y` coordinates.
+ **Face attributes** – A set of facial attributes, which includes: AgeRange, Beard, Emotions, Eyeglasses, EyesOpen, Gender, MouthOpen, Mustache, Smile, and Sunglasses. The value can be of different types, such as a Boolean type (whether a person is wearing sunglasses) or a string (whether the person is male or female). In addition, for most attributes, the response also provides a confidence in the detected value for the attribute. Note that while FaceOccluded and EyeDirection attributes are supported when using `DetectFaces`, they aren’t supported when analyzing videos with `StartFaceDetection` and `GetFaceDetection`.
+ **Timestamp** – The time that the face was detected in the video. 
+ **Paging information** – The example shows one page of face detection information. You can specify how many person elements to return in the `MaxResults` input parameter for `GetFaceDetection`. If more results than `MaxResults` exist, `GetFaceDetection` returns a token (`NextToken`) that's used to get the next page of results. For more information, see [Getting Amazon Rekognition Video analysis results](api-video.md#api-video-get).
+ **Video information** – The response includes information about the video format (`VideoMetadata`) in each page of information that's returned by `GetFaceDetection`.
+ **Quality** – Describes the brightness and the sharpness of the face.
+ **Pose** – Describes the rotation of the face.

```
{
    "Faces": [
        {
            "Face": {
                "BoundingBox": {
                    "Height": 0.23000000417232513,
                    "Left": 0.42500001192092896,
                    "Top": 0.16333332657814026,
                    "Width": 0.12937499582767487
                },
                "Confidence": 99.97504425048828,
                "Landmarks": [
                    {
                        "Type": "eyeLeft",
                        "X": 0.46415066719055176,
                        "Y": 0.2572723925113678
                    },
                    {
                        "Type": "eyeRight",
                        "X": 0.5068183541297913,
                        "Y": 0.23705792427062988
                    },
                    {
                        "Type": "nose",
                        "X": 0.49765899777412415,
                        "Y": 0.28383663296699524
                    },
                    {
                        "Type": "mouthLeft",
                        "X": 0.487221896648407,
                        "Y": 0.3452930748462677
                    },
                    {
                        "Type": "mouthRight",
                        "X": 0.5142884850502014,
                        "Y": 0.33167609572410583
                    }
                ],
                "Pose": {
                    "Pitch": 15.966927528381348,
                    "Roll": -15.547388076782227,
                    "Yaw": 11.34195613861084
                },
                "Quality": {
                    "Brightness": 44.80223083496094,
                    "Sharpness": 99.95819854736328
                }
            },
            "Timestamp": 0
        },
        {
            "Face": {
                "BoundingBox": {
                    "Height": 0.20000000298023224,
                    "Left": 0.029999999329447746,
                    "Top": 0.2199999988079071,
                    "Width": 0.11249999701976776
                },
                "Confidence": 99.85971069335938,
                "Landmarks": [
                    {
                        "Type": "eyeLeft",
                        "X": 0.06842322647571564,
                        "Y": 0.3010137975215912
                    },
                    {
                        "Type": "eyeRight",
                        "X": 0.10543643683195114,
                        "Y": 0.29697132110595703
                    },
                    {
                        "Type": "nose",
                        "X": 0.09569807350635529,
                        "Y": 0.33701086044311523
                    },
                    {
                        "Type": "mouthLeft",
                        "X": 0.0732642263174057,
                        "Y": 0.3757539987564087
                    },
                    {
                        "Type": "mouthRight",
                        "X": 0.10589495301246643,
                        "Y": 0.3722417950630188
                    }
                ],
                "Pose": {
                    "Pitch": -0.5589138865470886,
                    "Roll": -5.1093974113464355,
                    "Yaw": 18.69594955444336
                },
                "Quality": {
                    "Brightness": 43.052337646484375,
                    "Sharpness": 99.68138885498047
                }
            },
            "Timestamp": 0
        },
        {
            "Face": {
                "BoundingBox": {
                    "Height": 0.2177777737379074,
                    "Left": 0.7593749761581421,
                    "Top": 0.13333334028720856,
                    "Width": 0.12250000238418579
                },
                "Confidence": 99.63436889648438,
                "Landmarks": [
                    {
                        "Type": "eyeLeft",
                        "X": 0.8005779385566711,
                        "Y": 0.20915353298187256
                    },
                    {
                        "Type": "eyeRight",
                        "X": 0.8391435146331787,
                        "Y": 0.21049551665782928
                    },
                    {
                        "Type": "nose",
                        "X": 0.8191410899162292,
                        "Y": 0.2523227035999298
                    },
                    {
                        "Type": "mouthLeft",
                        "X": 0.8093273043632507,
                        "Y": 0.29053622484207153
                    },
                    {
                        "Type": "mouthRight",
                        "X": 0.8366993069648743,
                        "Y": 0.29101791977882385
                    }
                ],
                "Pose": {
                    "Pitch": 3.165884017944336,
                    "Roll": 1.4182015657424927,
                    "Yaw": -11.151537895202637
                },
                "Quality": {
                    "Brightness": 28.910892486572266,
                    "Sharpness": 97.61507415771484
                }
            },
            "Timestamp": 0
        }.......

    ],
    "JobStatus": "SUCCEEDED",
    "NextToken": "i7fj5XPV/fwviXqz0eag9Ow332Jd5G8ZGWf7hooirD/6V1qFmjKFOQZ6QPWUiqv29HbyuhMNqQ==",
    "VideoMetadata": {
        "Codec": "h264",
        "DurationMillis": 67301,
        "FileExtension": "mp4",
        "Format": "QuickTime / MOV",
        "FrameHeight": 1080,
        "FrameRate": 29.970029830932617,
        "FrameWidth": 1920
    }
}
```