Implementing real-time face recognition with Nashorn

06 Oct 2023

In this blog post, we will explore how to implement real-time face recognition using Nashorn, a JavaScript engine for Java. Face recognition is a fascinating technology that has numerous applications in areas such as security, computer vision, and user authentication. By leveraging Nashorn, we can combine the power of JavaScript with the performance of Java to build a real-time face recognition system.

What is Nashorn?
Setting up the Project
Using OpenCV for Face Detection
Implementing Face Recognition
Integration with Nashorn
Conclusion

What is Nashorn? (#what-is-nashorn)

Nashorn is a JavaScript engine that is bundled with Java 8 and later versions. It allows you to execute JavaScript code within Java applications. Nashorn provides seamless integration between JavaScript and Java, allowing you to utilize the power of both languages in a single application.

Setting up the Project (#setting-up-the-project)

First, let’s set up the project by creating a new Maven project and adding the necessary dependencies. We will need the following dependencies:

Nashorn: for executing JavaScript code
OpenCV: for face detection and recognition

Add the following dependencies to your project’s pom.xml:

<dependencies>
  <dependency>
    <groupId>jdk.internal.scripting</groupId>
    <artifactId>nashorn-core</artifactId>
    <version>8.0.272</version>
  </dependency>
  <dependency>
    <groupId>org.opencv</groupId>
    <artifactId>opencv</artifactId>
    <version>4.5.1</version>
  </dependency>
</dependencies>

Using OpenCV for Face Detection (#using-opencv-for-face-detection)

OpenCV is a popular computer vision library that provides various algorithms for image and video processing. We will use OpenCV for face detection in our face recognition system.

To use OpenCV in Nashorn, we need to load the OpenCV library and initialize it. Add the following code to initialize OpenCV:

// Load the OpenCV library
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

Now, we can use the OpenCV APIs to perform face detection on images or video streams.

Implementing Face Recognition (#implementing-face-recognition)

To implement face recognition, we need a dataset of known faces to compare against. We can create a simple database with the names and corresponding images of the known faces.

We can use a pre-trained neural network model like VGGFace or FaceNet to extract facial features from the known faces. Once we have the facial features, we can compare them with the features of the detected faces to identify a match.

Integration with Nashorn (#integration-with-nashorn)

To integrate face recognition with Nashorn, we can use the javax.script package to evaluate JavaScript code. We can write the face recognition logic in JavaScript and invoke it from our Java application using Nashorn.

Here’s an example of how you can integrate face recognition with Nashorn:

ScriptEngineManager manager = new ScriptEngineManager();
ScriptEngine engine = manager.getEngineByName("nashorn");

// Execute the JavaScript code
engine.eval(new FileReader("face-recognition.js"));

// Invoke a function defined in the JavaScript code
Invocable invocable = (Invocable) engine;
invocable.invokeFunction("performFaceRecognition");

Conclusion (#conclusion)

In this blog post, we explored how to implement real-time face recognition using Nashorn. We learned about Nashorn, set up the project, used OpenCV for face detection, implemented face recognition, and integrated it with Nashorn. By combining JavaScript and Java, we can harness the power of both languages to build a robust face recognition system.

Implementing real-time face recognition can have many practical applications in fields like security, attendance systems, and personalized user experiences. With Nashorn, we can leverage the flexibility of JavaScript and the performance of Java to create powerful and efficient face recognition solutions. Start experimenting with Nashorn and unlock the potential of real-time face recognition in your applications.

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