Face Detection, Face Recognition using Node.js
In this quickstart, you will use the Azure Face REST API with Node.js to detect human faces in an image.
Prerequisites
A Face API subscription key. You can get a free trial subscription key from Try Cognitive Services. Or, follow the instructions in Create a Cognitive Services account to subscribe to the Face API service and get your key.
A code editor such as Visual Studio Code
Set up the Node environment
Go to the folder where you’d like to create your project and create a new file, facedetection.js. Then install the requests module to this project. This allows your scripts to make HTTP requests.
npm install request --save
Write the Node.js script
Paste the following code into facedetection.js. These fields specify how to connect to the Face service and where to get the input data. You’ll need to update the subscriptionKey field with the value of your subscription key, and you need to change the uriBase string so that it contains the correct endpoint string. You may wish to change the imageUrl field to point to your own input image.
'use strict';
const request = require('request');
// Replace <Subscription Key> with your valid subscription key.
const subscriptionKey = '<Subscription Key>';
// You must use the same location in your REST call as you used to get your
// subscription keys. For example, if you got your subscription keys from
// westus, replace "westcentralus" in the URL below with "westus".
const uriBase = 'https://<My Endpoint String>.com/face/v1.0/detect';
const imageUrl =
'https://upload.wikimedia.org/wikipedia/commons/3/37/Dagestani_man_and_woman.jpg';
Then, add the following code to call the Face API and get face attribute data from the input image. The returnFaceAttributes field specifies which face attributes to retrieve. You may wish to change this string depending on your intended use.
// Request parameters.
const params = {
'returnFaceId': 'true',
'returnFaceLandmarks': 'false',
'returnFaceAttributes': 'age,gender,headPose,smile,facialHair,glasses,' +
'emotion,hair,makeup,occlusion,accessories,blur,exposure,noise'
};
const options = {
uri: uriBase,
qs: params,
body: '{"url": ' + '"' + imageUrl + '"}',
headers: {
'Content-Type': 'application/json',
'Ocp-Apim-Subscription-Key' : subscriptionKey
}
};
request.post(options, (error, response, body) => {
if (error) {
console.log('Error: ', error);
return;
}
let jsonResponse = JSON.stringify(JSON.parse(body), null, ' ');
console.log('JSON Response\n');
console.log(jsonResponse);
});
Save and run the script
After you’ve made your changes, open a command prompt and run the file with the node command.
node facedetection.js
You should see the face information displayed as JSON data in the console window. For example:
[
{
"faceId": "ae8952c1-7b5e-4a5a-a330-a6aa351262c9",
"faceRectangle": {
"top": 621,
"left": 616,
"width": 195,
"height": 195
},
"faceAttributes": {
"smile": 0,
"headPose": {
"pitch": 0,
"roll": 6.8,
"yaw": 3.7
},
"gender": "male",
"age": 37,
"facialHair": {
"moustache": 0.4,
"beard": 0.4,
"sideburns": 0.1
},
"glasses": "NoGlasses",
"emotion": {
"anger": 0,
"contempt": 0,
"disgust": 0,
"fear": 0,
"happiness": 0,
"neutral": 0.999,
"sadness": 0.001,
"surprise": 0
},
"blur": {
"blurLevel": "high",
"value": 0.89
},
"exposure": {
"exposureLevel": "goodExposure",
"value": 0.51
},
"noise": {
"noiseLevel": "medium",
"value": 0.59
},
"makeup": {
"eyeMakeup": true,
"lipMakeup": false
},
"accessories": [],
"occlusion": {
"foreheadOccluded": false,
"eyeOccluded": false,
"mouthOccluded": false
},
"hair": {
"bald": 0.04,
"invisible": false,
"hairColor": [
{
"color": "black",
"confidence": 0.98
},
{
"color": "brown",
"confidence": 0.87
},
{
"color": "gray",
"confidence": 0.85
},
{
"color": "other",
"confidence": 0.25
},
{
"color": "blond",
"confidence": 0.07
},
{
"color": "red",
"confidence": 0.02
}
]
}
}
},
{
"faceId": "b1bb3cbe-5a73-4f8d-96c8-836a5aca9415",
"faceRectangle": {
"top": 693,
"left": 1503,
"width": 180,
"height": 180
},
"faceAttributes": {
"smile": 0.003,
"headPose": {
"pitch": 0,
"roll": 2,
"yaw": -2.2
},
"gender": "female",
"age": 56,
"facialHair": {
"moustache": 0,
"beard": 0,
"sideburns": 0
},
"glasses": "NoGlasses",
"emotion": {
"anger": 0,
"contempt": 0.001,
"disgust": 0,
"fear": 0,
"happiness": 0.003,
"neutral": 0.984,
"sadness": 0.011,
"surprise": 0
},
"blur": {
"blurLevel": "high",
"value": 0.83
},
"exposure": {
"exposureLevel": "goodExposure",
"value": 0.41
},
"noise": {
"noiseLevel": "high",
"value": 0.76
},
"makeup": {
"eyeMakeup": false,
"lipMakeup": false
},
"accessories": [],
"occlusion": {
"foreheadOccluded": false,
"eyeOccluded": false,
"mouthOccluded": false
},
"hair": {
"bald": 0.06,
"invisible": false,
"hairColor": [
{
"color": "black",
"confidence": 0.99
},
{
"color": "gray",
"confidence": 0.89
},
{
"color": "other",
"confidence": 0.64
},
{
"color": "brown",
"confidence": 0.34
},
{
"color": "blond",
"confidence": 0.07
},
{
"color": "red",
"confidence": 0.03
}
]
}
}
}
]
Next steps
In this quickstart, you wrote a Node.js script that calls the Azure Face API to detect faces in an image and return their attributes. Next, explore the Face API reference documentation to learn more.
#node-js #javascript #machine-learning
