A Guide to Building Java-Based Facial Recognition Software for Developers

Real-time facial recognition involves identifying or verifying an individual's identity and digitally imaging their face via a live video. Facial recognition apps are used in a wide variety of applications, ranging from security to surveillance systems, to streamline identity verification. OpenCV, or Open Source Computer Vision, is an open-source library that provides various tools and machine learning algorithms for image and video processing in the Java framework. It includes special functions for detection and recognition and aids in custom face recognition software development. 

What Is A Face Recognition App?

A face recognition app is a user-facing interface that scans a subject's facial characteristics using machine learning techniques. It compares the face being digitally imaged with a database of already scanned faces to prevent duplicates and cross-check identities. Facial recognition technology is the building block of many digital identification, verification, and grouping services. It can support many practical use cases, both for businesses and users at home or work. 

In What Way Does Facial Recognition System Operate? 

Face recognition software identifies or verifies subjects by analyzing their facial characteristics via live audio or video processing streams. User identification is done to grant them authorized access to sensitive resources in organizations' systems. 

Facial recognition technology (FRT) combines gesture recognition, facial biometric analysis, and movement detection to ascertain the individual's identity. It reads the person's expressions on-screen and collects a unique set of facial biometrics associated with them. Later, the person can use these biometrics to automatically verify or authenticate and get instant access to confidential services.  

Practical Uses of Face Recognition Software 

Facial recognition systems are used to check and verify the identities of passengers traveling to airports. Airport authorities prefer a face recognition app since it streamlines identity management and is almost twice as fast as fingerprint scanning. Border police can run random verification tests in real-time, and in the United States alone, more than 15 airports have facial recognition systems and software set up in terminals. Europe doesn't keep electronic records, and border guards use facial recognition software to allow entry or exit. 

In the healthcare industry, facial recognition is used to access patient records and streamline the patient registration process. Facial recognition apps can detect front and side profiles and identify individual faces from images containing two or more other people's faces.   

Hotel kiosks in restaurants and bars use this technology to grant physical access to their premises. It can be especially useful to restrict entry during crises like pandemics, such as in the case of COVID-19 lockdowns. There is a low risk of impersonation, and all customers are identified at different entry and exit points. Security personnel in shopping malls and large complexes also leverage facial recognition apps to restrict unauthorized entry. 

How to Make Facial Recognition Software 

Here is a beginner's guide to making facial recognition software. For this tutorial, we will use Java. Download and install the Java Development Kit (JDK), Eclipse IDE, and OpenCV library for Java. We will also need TensorFlow. 

1. We can start writing the code once you have loaded and installed these tools. Load the FaceNet model. You can load it by copy-pasting the following code in your development environment: 

     
   import org.tensorflow.Graph;
   import org.tensorflow.Session;
   import org.tensorflow.Tensor;
   import org.tensorflow.TensorFlow;
   public class FaceNetModel {
     private Graph graph;
     private Session session;
     private String modelPath;
     public FaceNetModel(String modelPath) {
       this.modelPath = modelPath;
     }
     public void loadModel() {
       graph = new Graph();
       byte[] modelBytes = readAllBytesOrExit(Paths.get(modelPath));
       graph.importGraphDef(modelBytes);
       session = new Session(graph);
     }
     public float[] getFaceEmbedding(Mat face) {
       float[] embedding = null;
       try (Tensor < Float > tensor = normalizeImage(face)) {
         Tensor < Float > output = session.runner()
           .feed("input_1", tensor)
           .fetch("Bottleneck_BatchNorm/batchnorm/add_1")
           .run()
           .get(0)
           .expect(Float.class);
         embedding = new float[(int) output.shape()[1]];
         output.copyTo(embedding);
       } catch (Exception e) {
         e.printStackTrace();
       }
       return embedding;
     }
     private Tensor < Float > normalizeImage(Mat mat) {
       Imgproc.cvtColor(mat, mat, Imgproc.COLOR_BGR2RGB);
       mat.convertTo(mat, CvType.CV_32F);
       Core.divide(mat, Scalar.all(255.0 f), mat);
       return Tensor.create(mat.reshape(1, 160, 160, 3));
     }
     private static byte[] readAllBytesOrExit(Path path) {
       try {
         return Files.readAllBytes(path);
       } catch (IOException e) {
         e.printStackTrace();
         System.exit(-1);
       }
       return null;
     }
   }
     

2. Load the face recognition database. Use the following code for this:
   
   

   private Map<string, float[]=""> faceDb = new HashMap<>();   
   public void loadFaceDb(String dbPath) {   
       try {   
           BufferedReader reader = new BufferedReader(new FileReader(dbPath));   
           String line;   
           while ((line = reader.readLine()) != null) {   
               String[] values = line.split(",");   
               String name = values[0];   
               float[] embedding = Arrays.stream(values[1].split(" ")).map(Float::parseFloat).toArray(float[]::new);   
               faceDb.put(name, embedding);   
           }   
           reader.close();   
       } catch (IOException e) {   
           e.printStackTrace();   
       }   
   } 
     </string,>

3. After loading the database, we need to implement facial recognition. This involves detecting faces in video streams, encoding and embedding them and comparing those embeddings with the images stored in the database. To do this and verify people in images, use the following code:
   
   

   private FaceNetModel faceNetModel = new FaceNetModel("facenet.pb");   
    
   public void recognizeFaces(Mat frame) {   
       Mat gray = new Mat();   
       Imgproc.cvtColor(frame, gray, Imgproc.COLOR_BGR2GRAY);   
       MatOfRect faces = new MatOfRect();   
       cascadeClassifier.detectMultiScale(gray, faces, 1.3, 5);   
       for (Rect rect : faces.toArray()) {   
           Mat face = new Mat(frame, rect);   
           Imgproc.resize(face, face, new Size(160, 160));   
           float[] embedding = faceNetModel.getFaceEmbedding(face);   
           String name = recognizeFace(embedding);   
           Imgproc.putText(frame, name, new Point(rect.x, rect.y - 10), Imgproc.FONT_HERSHEY_SIMPLEX, 1, new Scalar(0, 255, 0), 2);   
           Imgproc.rectangle(frame, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0), 2);   
       }   
   }   
      
   private String recognizeFace(float[] embedding) {   
       String name = "Unknown";   
       double minDistance = Double.MAX_VALUE;   
       for (Map.Entry<string, float[]=""> entry : faceDb.entrySet()) {   
           float[] dbEmbedding = entry.getValue();   
           double distance = calculateDistance(embedding, dbEmbedding);   
           if (distance < minDistance) {   
               minDistance = distance;   
               name = entry.getKey();   
           }  
       }   
       if (minDistance > threshold) {   
           name = "Unknown";   
       }   
       return name;   
   }   
   
   private double calculateDistance(float[] embedding1, float[] embedding2) {   
       double sum = 0.0;   
       for (int i = 0; i < embedding1.length; i++) {   
           sum += Math.pow(embedding1[i] - embedding2[i], 2);   
       }   
       return Math.sqrt(sum);   
   } 
   </string,>

Our face recognition app is now ready, and we can use it!

This program detects faces within the frame and pinpoints facial landmarks. It can rotate, zoom, locate specific marks on faces, and even detect eye colors and nose shapes. It's a great starting point for developing face recognition software and is very accurate.  

You can also build facial recognition software using Python instead of Java. If you want to learn more about how to build face recognition software, you can install the OpenCV library in Python and create face detection functions.  

Cost of Developing Face Recognition Software 

The cost of developing essential face recognition software can start at USD 1,000. The price can increase depending on the number of features added, cross-platform support, and more. Global enterprises demand face recognition, and with the integration of AI services, we can expect this trend to continue.  

The global facial recognition software market is forecasted to be valued at USD 4.5 billion by 2025 and grow at a CAGR of 17.2% annually. As technologies constantly evolve, there are also hidden expenses associated with the development of these software solutions. 

Top Tools to Create a Face Recognition App 

You can build your face recognition app in Python or use various open-source tools and libraries to develop and deploy these solutions. Face recognition software uses AI and ML technologies to verify identities. It scans "facial landmarks" to combine and create codes that are unique to each person. 

The top tools used to create a face recognition app in 2024 are: 

1. Amazon Rekognition
   
   Amazon Rekognition identifies objects and scenes by assigning them labels. It allows users to add custom labels to objects and delivers content moderation capabilities. PPE detection, text detection, face search and recognition, and storage are included. It comes with plenty of documentation, tutorials, and samples. The pricing model depends on the number of faces analyzed and is not fixed. The free tier lasts 12 months, allowing users to analyze up to 5,000 faces per month and store up to 1,000 pieces of face metadata.
   
   
2. Betaface
   
   Betaface offers users face and object recognition features. It is one of the top face recognition app development services. It has three core services: customer software development services, facial recognition SDKs, and hosted web services. Betaface can perform facial feature tracking and is best used in video surveillance and security software solutions.
   
   Its free API allows users to use up to 500 images per day, and it offers three monthly subscription plans. Betaface is a great example of how a facial recognition system works.
   
   
3. BioID
   
   BioID is a GDPR-compliant facial recognition engine that provides biometrics as a service. It offers cloud-based FRS services accessed via APIs. The BioID web service can be deployed on-premise or in the cloud.
   
   BioID's liveness detection service is precious for businesses. It can be used to prevent online fraud and identity threats and detect user presence using face, eye, and voice recognition. Pricing for the development of facial recognition software is based on a pay-per-user level. The company also offers other core services, such as PhotoVerify and Deepfake Detection, and it has over 25 years of experience.  
   
   
4. Microsoft Azure Face API
   
   Microsoft Azure Face API is another excellent tool for detecting and recognizing human faces in images. It offers face recognition, face grouping, and face verification capabilities and can even organize faces into groups based on visual similarities. The Azure Face API offers custom pricing options, and it can also be used to ID faces based on emotion, gender, age, and motion understanding. You can learn how to make face recognition software by trying out its different features and exploring its human-computer interaction elements a bit. 
   
   
   

Conclusion  

You can hire Java developers or try out offshore software development services to build your custom face recognition app. Remember, there is no one-size-fits-all solution, and your requirements will vary depending on your business's use cases. A face recognition app for the healthcare industry will not be the same as retail or eCommerce. Hiring dedicated software developers who understand your unique requirements and can translate them to the right products is especially important. 

 You can contact Clarion Technologies today to get started. 

Research: 

• https://www.signicat.com/blog/face-recognition 
• https://opencv-java-tutorials.readthedocs.io/en/stable/08%20-%20Face%20Recognition%20and%20Tracking.html 
• https://www.javatpoint.com/real-time-face-recognition-in-java 
• https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-face-recognition 
• https://www.thalesgroup.com/en/markets/digital-identity-and-security/government/inspired/where-facial-recognition-used 
• https://itrexgroup.com/blog/how-much-does-a-facial-recognition-system-cost/#