Face Comparison

Compare two faces of the same person and return a confidence score of the similarity.

Applicable scenarios

Applicable to customer authentication, photo classification and other scenarios, such as self-service hotel check-in, personnel check-in, campus entrance passage, and photo album production.

API reference

HTTP request method: POST
Request body parameters

Name	Type	Required	Description
url	String	Choose url or img.	Image URL address, which supports HTTP/HTTPS and S3 protocols. Supported image formats are jpg/jpeg/png/bmp, with the longest side not exceeding 4096px.
img	String	Choose url or img.	Base64 encoded image data.

Example JSON request

{
  "url": "Image URL address"
}

{
  "img": "Base64-encoded image data"
}

Response parameters

Name	Type	Description
Faces	List	List of detected faces in the image.
face_hash	List	List with 768 parameters for a 768-dimensional face vector.
BoundingBox	JSON	Coordinate values of the face in the image, including the percentage of top, left, width, height relative to the full screen.
Confidence	Float	Confidence score of the recognized face, which is a float type value between 0 and 1.
FaceModelVersion	String	Current model version.

Example JSON response

{
    "Faces": [
        {
            "BoundingBox": {
                "Width": 0.057923507690429685, 
                "Height": 0.10426715253778117, 
                "Left": 0.5258836364746093, 
                "Top": 0.40569204600369024
            }, 
            "Confidence": 0.8736226558685303, 
            "face_hash": [64.8125, -86.8125, -9.84375, 12.390625, 161.625, ..., 4.8046875
            ]
        }, 
        {
            "BoundingBox": {
                "Width": 0.04332921028137207, 
                "Height": 0.10577215250117152, 
                "Left": 0.1566245174407959, 
                "Top": 0.6526811308355788
            }, 
            "Confidence": 0.8055327534675598, 
            "face_hash": [61.21875, -33.84375, -36.71875, 70.625, 110.125, ..., -28.421875
            ]
        }
    ], 
    "FaceModelVersion": "1.2.0"
}

API test

You can use the following tools (API explorer, Postman, cURL, Python, Java) to test calling APIs.

API Explorer

Prerequisites

When deploying the solution, you need to：

set the parameter API Explorer to yes.
set the parameter API Gateway Authorization to NONE.

Otherwise, you can only view the API definitions in the API explorer, but cannot test calling API online.

Steps

Sign in to the AWS CloudFormation console.
On the Stacks page, select the solution’s root stack. Do not select the NESTED stack.
Choose the Outputs tab, and find the URL for APIExplorer.
Click the URL to access the API explorer. The APIs that you have selected during deployment will be displayed.
For the API you want to test, click the down arrow to display the request method.
Choose the Try it out button, and enter the correct Body data to test API and check the test result.
Make sure the format is correct, and choose Execute.
Check the returned result in JSON format in the Responses body. If needed, copy or download the result.
Check the Response headers.
(Optional) Choose Clear next to the Execute button to clear the request body and responses.

Postman (AWS_IAM Authentication）

Sign in to the AWS CloudFormation console.
On the Stacks page, select the solution’s root stack.
Choose the Outputs tab, and find the URL with the prefix GeneralOCR.
Create a new tab in Postman. Paste the URL into the address bar, and select POST as the HTTP call method.
Open the Authorization configuration, select Amazon Web Service Signature from the drop-down list, and enter the AccessKey, SecretKey and Amazon Web Service Region of the corresponding account (such as cn-north-1 or cn-northwest-1 ).
Open the Body configuration item and select the raw and JSON data types.
Enter the test data in the Body, and click the Send button to see the corresponding return results.

{
  "url": "Image URL address"
}

cURL

Windows

curl --location --request POST "https://[API_ID].execute-api.[AWS_REGION].amazonaws.com/[STAGE]/face_comparison" ^
--header "Content-Type: application/json" ^
--data-raw "{\"url\": \"Image URL address\"}"

Linux/MacOS

curl --location --request POST 'https://[API_ID].execute-api.[AWS_REGION].amazonaws.com/[STAGE]/face_comparison' \
--header 'Content-Type: application/json' \
--data-raw '{
  "url":"Image URL address"
}'

Python (AWS_IAM Authentication)

import requests
import json
from aws_requests_auth.boto_utils import BotoAWSRequestsAuth

auth = BotoAWSRequestsAuth(aws_host='[API_ID].execute-api.[AWS_REGION].amazonaws.com',
                           aws_region='[AWS_REGION]',
                           aws_service='execute-api')

url = 'https://[API_ID].execute-api.[AWS_REGION].amazonaws.com/[STAGE]/face_comparison'
payload = {
    'url': 'Image URL address'
}
response = requests.request("POST", url, data=json.dumps(payload), auth=auth)
print(json.loads(response.text))

Python (NONE Authentication)

import requests
import json

url = "https://[API_ID].execute-api.[AWS_REGION].amazonaws.com/[STAGE]/face_comparison"

payload = json.dumps({
  "url": "Image URL address"
})
headers = {
  'Content-Type': 'application/json'
}

response = requests.request("POST", url, headers=headers, data=payload)

print(response.text)

Java

OkHttpClient client = new OkHttpClient().newBuilder()
  .build();
MediaType mediaType = MediaType.parse("application/json");
RequestBody body = RequestBody.create(mediaType, "{\n  \"url\":\"Image URL address\"\n}");
Request request = new Request.Builder()
  .url("https://xxxxxxxxxxx.execute-api.xxxxxxxxx.amazonaws.com/[STAGE]/face_comparison")
  .method("POST", body)
  .addHeader("Content-Type", "application/json")
  .build();
Response response = client.newCall(request).execute();

Cost estimation

You are responsible for the cost of using each Amazon Web Services service when running the solution. As of this revision, the main cost factors affecting the solution include.

Amazon API Gateway calls
Amazon API Gateway data output
Amazon CloudWatch Logs storage
Amazon Elastic Container Registry storage

If you choose an Amazon Lambda based deployment, the factors also include:

Amazon Lambda invocations
Amazon Lambda running time

If you choose an Amazon SageMaker based deployment, the factors also include:

Amazon SageMaker endpoint node instance type
Amazon SageMaker endpoint node data input
Amazon SageMaker endpoint node data output

Cost estimation example 1

In AWS China (Ningxia) Region operated by NWCD (cn-northwest-1), process an image of 1MB in 1 second

The cost of using this solution to process the image is shown below:

Service	Dimensions	Cost
AWS Lambda	1 million invocations	¥1.36
AWS Lambda	8192MB memory, 1 seconds run each time	¥907.8
Amazon API Gateway	1 million invocations	¥28.94
Amazon API Gateway	100KB data output each time, ¥0.933/GB	¥93.3
Amazon CloudWatch Logs	10KB each time, ¥6.228/GB	¥62.28
Amazon Elastic Container Registry	0.5GB storage, ¥0.69/GB each month	¥0.35
Total		¥1010.06

Cost estimation example 2

In US East (Ohio) Region (us-east-2), process an image of 1MB in 1 seconds

The cost of using this solution to process this image is shown below:

Service	Dimensions	Cost
AWS Lambda	1 million invocations	$0.20
AWS Lambda	8192MB memory, 1 seconds run each time	$133.3
Amazon API Gateway	1 million invocations	$3.5
Amazon API Gateway	100KB data output each time, $0.09/GB	$9
Amazon CloudWatch Logs	10KB each time, $0.50/GB	$5
Amazon Elastic Container Registry	0.5GB存储，$0.1/GB each month	$0.05
Total		$142.95

Uninstall the deployment

You can uninstall the Face Comparison feature via Amazon CloudFormation as described in Add or remove AI features and make sure the FaceComparison parameter is set to no in the parameters section.

Note

Time to uninstall the deployment is approximately: 6 Minutes