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.
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.
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.
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.
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.
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;
}
}
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,>
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.
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.
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.
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.
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