Face Compare

Compare between the two images if the person in the image is the same person.

Image Requirements

Type : JPG (JPEG), PNG Format : Base64 Size : Must be over 112*112 pixels Minimum size of face : The bounding size of a detected face should be over than 112 pixels.

Request Parameter

	Name	Type	Description
Required	image1	String	Base64 encoded binary data of first image.
Required	image2	String	Base64 encoded binary data of second image.
Optional	verified_document	Boolean	Whether or not verify that in images have ID card. Note: default value is False.
Optional	image1_is_document	Boolean	To verify whether an image in image1 parameter has ID Card. Note: default value is False.
Optional	image2_is_document	Boolean	To verify whether an image in image2 parameter has ID Card. Note: default value is False.
Optional	blur_detection	Boolean	Whether or not detect blurriness from images. Note: default value is False.

Return Values

Fields	Type	Description
request_id	String	Unique id each request
confidence	Float	Indicates the similarity of two faces, a floating-point number with 2 decimal places between [0,100]. Higher confidence indicates higher possibility that two faces belong to same person. Note: if no face is detected within image uploaded, this string will not be returned.
thresholds	Object	A set of thresholds including 3 floating-point numbers with 2 decimal places between [0,100]. If the confidence does not meet the "err_01" threshold, it is highly suggested that the two faces are not from the same person. While if the confidence is beyond the "err_0001" threshold, there's high possibility that they are from the same person. err_01: confidence threshold at the 0.1% error rate; err_001: confidence threshold at the 0.01% error rate; err_0001: confidence threshold at the 0.001% error rate; Note: seeing that thresholds are not static, there's no need to store values of thresholds in a persistent form, especially not to compare the confidence with a previously returned threshold. If no face is detected within image uploaded, this string will not be returned.
time_used	Float	Duration. Unit: second
error_message	String	This string will not be returned unless request fails. For more details, please see the following section on error message.

Error Message

HTTP Status	Error Message	Description
400	IMAGE_ERROR_UNSUPPORTED_FORMAT: <param>	The image which <param> indicates can not be resolved. The file format may not be supported or the file is damaged.
200	CAN_NOT_DETECT_FACE: <param>	The image which <param> does not have face.
200	IMAGE_BLURED: <param>	The image which <param> is blured.
200	CAN_NOT_DETECT_IDCARD: <param>	The image which <param> does not have ID card.

Compare between the two images if the person in the image is the same person.

POST https://apis.aigen.online/aiface/face-compare/v1

​

Request Body

Name	Type	Description
image1*	String	​Base64 encoded binary data of first image.
image2*	String	Base64 encoded binary data of second image.
verified_document	Boolean	Whether or not verify that in images have ID card. Note: default value is False.
image1_is_document	Boolean	To verify whether an image in image1 parameter has ID Card. Note: default value is False.
image2_is_document	Boolean	To verify whether an image in image2 parameter has ID Card. Note: default value is False.
blur_detection	Boolean	Whether or not detect blurriness from images. Note: default value is False.

200: OK

{
  "request_id": "string",
  "confidence": 100,
  "thresholds": {
    "err_01": 45.92,
    "err_001": 54.36,
    "err_0001": 59.71
  },
  "time_used": 0,
  "error_message": "error_message"
}

422: Unprocessable Entity

{
  "detail": [
    {
      "loc": [
        "string"
      ],
      "msg": "string",
      "type": "string"
    }
  ]
}

Python

import requests
import json

api = "https://apis.aigen.online/aiface/face-compare/v1"
headers = {"x-aigen-key": "<key>", "content-type": "application/json"}
data = json.dumps({
    "image1": "<base64_string>",
    "image2": "<base64_string>",
})

res = requests.post(api, data=data, headers=headers)
print(res.json())
​

Nodejs

const axios = require("axios");

const api = "https://apis.aigen.online/aiface/face-compare/v1";
const headers = {
  "x-aigen-key": "<key>",
};
const data = { 
  image1: "<bas64_string>",
  image2: "<bas64_string>",
};

axios
  .post(api, data, { headers: headers })
  .then((res) => {
    console.log(res.data);
  })
  .catch((err) => {
    console.error(err.response.data);
  });