How to do Face Recognition with JavaScript

In this article, you'll learn how to integrate Eden AI's Face Recognition API with JavaScript. This easy-to-use API enables you to identify and verify faces in images, enhancing your security and authentication systems with cutting-edge AI technology.

What is Face Recognition API?

‍

The Face Recognition API is a powerful tool that leverages advanced computer vision, artificial intelligence, and machine learning techniques to detect, identify, and verify faces in images and videos.

This API is highly versatile, with applications spanning security systems, personalized user experiences, authentication processes, law enforcement, and even social media platforms for tagging individuals in photos.

By analyzing and matching facial features, it accurately identifies individuals by comparing an input face to a database of stored faces.

Whether working with surveillance footage, photos, or real-time video streams, the Face Recognition API enhances your ability to monitor, analyze, and secure visual data, making it an invaluable asset for various industries.

Applications of Face Recognition

• Security & Authentication: Face recognition is widely used for secure access control in smartphones, laptops, and restricted areas, replacing passwords and keycards.
• Surveillance & Law Enforcement: Police and security agencies use face recognition to identify suspects, monitor crowds, and enhance public safety.
• Banking & Finance: Financial institutions leverage face recognition for identity verification in mobile banking apps and ATMs to prevent fraud.
• Retail & Customer Experience: Stores use face recognition for personalized marketing, VIP customer recognition, and theft prevention.
• Healthcare: Hospitals utilize face recognition for patient identification, medical record access, and monitoring patient well-being

‍

How to do Face Recognition?

‍

Set Up Your Eden AI Account

1. Create an Account: If you don’t have an Eden AI account yet, sign up for free using this link. After signing up, go to the dashboard to get your API key.

2. Navigate to Image Technologies – After logging in, go to the face recognition section of the platform.

3. Select Face Recognition – Choose the face recognition feature or explore advanced options like real-time detection and verification based on your needs.

‍

Implementing Face Recognition with JavaScript

‍

For our implementation, we’ll use the axios library, which is a popular promise-based HTTP client for JavaScript. Axios allows us to easily make HTTP requests to the Eden AI API. You can install axios via npm:

npm install axios

‍

Code Example

const axios = require("axios").default;

const PROVIDER = "amazon";  // Specify which provider to use for face recognition

// Function to construct the payload for sending requests to Eden AI
function constructPayload(url, fileURL) {
  return {
    method: "POST",  // HTTP method: POST to send data
    url: url,  // API endpoint
    headers: {
      Authorization: "Bearer YOUR_API_KEY",  // Replace with your actual API key from Eden AI
    },
    data: {
      providers: PROVIDER,  // Specify the provider, e.g., Amazon
      file_url: fileURL,  // URL of the image you want to process
    },
  };
}

// Example: Adding faces to the database for recognition
const facesUrls = ["🔗 URL of face 1", "🔗 URL of face 2", "🔗 URL of face 3"];
facesUrls.forEach((faceUrl) => {
  const options = constructPayload("https://api.edenai.run/v2/image/face_recognition/add_face", faceUrl);
  axios.request(options);
});

// Example: Recognizing a specific face
const faceToRecognize = "🔗 URL of face to recognize";
const recognizeOptions = constructPayload(
  "https://api.edenai.run/v2/image/face_recognition/recognize",
  faceToRecognize
);

axios.request(recognizeOptions).then((response) => {
  const faceItems = response.data[PROVIDER].items;
  faceItems.forEach((faceItem) => {
    console.log("Face ID: ", faceItem.face_id);
    console.log("Confidence: ", faceItem.confidence);
  });
});

‍

1. Axios Import and Setup:
   
   • We import axios, which is used to send HTTP requests.
   • We define the constant PROVIDER to specify which face recognition service (like Amazon) we’re using.
2. Construct Payload Function:
   
   • The construct Payload function helps in building the request to Eden AI. It takes two parameters: the API endpoint URL and the file URL (the face image URL).
   • The API key is passed in the Authorization header for authentication.
   • The providers field specifies which face recognition provider will handle the task.
3. Adding Faces to the Database:
   
   • We define a list facesUrls, which contains the URLs of faces you want to store in Eden AI's face recognition database.
   • For each face URL, we send a POST request to the add_face endpoint (https://api.edenai.run/v2/image/face_recognition/add_face), registering the face in the system.
4. Recognizing Faces:
   
   • In this section, we specify the URL of the face we want to recognize (faceToRecognize).
   • We send a POST request to the recognize endpoint (https://api.edenai.run/v2/image/face_recognition/recognize) with the URL of the face image.
   • The API responds with the recognition results, which are logged to the console.

‍

POST and GET Requests in the Code

‍

There are both POST and GET requests used in the code. Let’s break them down:

POST Request:

• POST https://api.edenai.run/v2/image/face_recognition/add_face
  This request adds a face to the recognition database. It is sent every time you upload a face image that you want to register for future recognition. In the code, we’re sending face images by their URLs to this endpoint.
• POST https://api.edenai.run/v2/image/face_recognition/recognize
  This request sends a face image URL to the Eden AI API to check if it matches any faces in the system. The response will include possible matches and confidence levels.

GET Request:

• ‍GET https://api.edenai.run/v2/image/face_recognition/list_faces
  Although this is not used in the provided code, this GET request retrieves a list of all the faces that have been added to the Eden AI database. This would be useful if you wanted to fetch all faces stored for later processing.

‍

Interpreting the Results

When the face recognition request is processed, the API will return a response that contains the following fields:

• Face ID:
  Each face that has been recognized is assigned a unique ID. This allows the system to track faces across different recognition requests.
• Confidence:
  The confidence score indicates how likely it is that the recognized face matches a registered face. A higher confidence score (closer to 1) indicates a better match.

‍

Here’s an example of what the output might look like:

Face ID:  face_12345
Confidence:  0.98
Face ID:  face_67890
Confidence:  0.92

In this example, two faces were recognized with a confidence level of 98% and 92%. The higher the confidence, the more reliable the match.

‍

Why Eden AI is the Best Tool for Face Recognition

Eden AI stands out as a top choice for face recognition due to many advantages

Ease of Use

‍Eden AI's API is designed to be simple and developer-friendly. You don’t need to worry about complex machine learning algorithms—Eden AI handles the heavy lifting.

Multiple Providers

‍Eden AI integrates with several face recognition providers, giving you flexibility in terms of performance and pricing.

No Machine Learning Expertise Needed

‍You don’t need to be an expert in machine learning or deep learning to use the API. Everything is abstracted, allowing you to focus on your application’s core functionality.

Scalability

‍Eden AI’s cloud-based API can handle a vast number of face recognition requests, making it ideal for both small and large-scale applications.

‍

Conclusion

In this guide, we explored how to implement face recognition checks in JavaScript using Eden AI’s API. We covered how to add faces to the database and how to recognize faces using a simple HTTP request. The key operations include sending POST requests to add and recognize faces, and understanding the responses.

Eden AI is an excellent tool for developers who want to integrate face recognition without the complexity of building machine learning models from scratch. With the flexibility of choosing from different providers, ease of use, and the ability to scale, Eden AI is a perfect choice for adding face recognition to your applications.

‍

Next Step in Your Project

‍
The Eden AI team is ready to assist you with your Face Recognition integration project. Here’s how we can help:

• Scheduling a product demo: We can organize a demo and discussion to better understand your specific needs.
• Expert support and advice: Our team of experts can help you find the best combination of providers tailored to your bank check parsing requirements.
• Third-party platform integration: We can quickly develop connectors to integrate Eden AI into your existing platforms, ensuring a smooth implementation process.