PHP controls camera for real-time face recognition: ways to improve security

PHP controls the camera for real-time face recognition: Methods to improve security

Abstract:
With the advancement of technology, face recognition technology is gradually used in various fields, including the security field. This article will introduce how to use PHP language to control the camera for real-time face recognition to improve security. The article will include code examples for face detection and recognition using the OpenCV library.

Keywords: PHP, camera, real-time, face recognition, OpenCV

Introduction:
In today's society, ensuring people's safety has become an important task. Face recognition technology is being widely used in various industries because of its efficient and accurate characteristics. This article will focus on how to use PHP language to control the camera for real-time face recognition to improve security.

1. Build the environment
Before we begin, we need to ensure that the PHP and OpenCV libraries have been correctly built. Make sure your system supports PHP extensions. You can view loaded extensions by typing php -m on the command line. Then, download and install the OpenCV library, making sure it can be referenced correctly in the system.

2. Use PHP to control the camera
Using PHP to control the camera can be achieved by calling system commands. The following is a simple sample code:

<?php
function captureImage($filename) {
    exec("raspistill -o $filename");
}
  
function showImage($filename) {
    echo "<img src='$filename' alt='captured image'>";
}
  
$filename = "captured.jpg";
captureImage($filename);
showImage($filename);
?>

The above code uses the exec function to call the system's raspistill command, realizes the camera capture operation, and displays the captured image on the web page.

3. Use the OpenCV library for face detection
OpenCV is a well-known open source computer vision library that provides many powerful functions, including face detection. We can use OpenCV library for real-time face detection and recognition.

First, install the OpenCV extension in PHP and load the corresponding library file. Then use the following code to perform face detection:

<?php
$faceCascade = new CvCascade();
$faceCascade->load("haarcascade_frontalface_default.xml");

$camera = new CvCapture();
$frame = $camera->queryFrame();
$gray = $frame->convertColor(CV_BGR2GRAY);
$faces = $faceCascade->detectMultiScale($gray);

foreach ($faces as $face) {
    $frame->rectangle($face->x, $face->y, $face->x + $face->width, $face->y + $face->height);
}

$frame->showImage();
?>

The above code uses OpenCV's Haar Cascade classifier to perform face detection and uses a rectangle to mark the detected face area.

4. Combined with face recognition algorithm
Before performing real-time face recognition, we need to perform face training first. Training the model will result in a training file for face recognition. We can use OpenCV's LBP (Local Binary Patterns) algorithm for face training.

<?php
$images = glob("train_images/*.jpg");
$labels = [0, 0, 1, 1]; // 训练集对应的标签

$lbph = new CvLBPHFaceRecognizer();
$lbph->train($images, $labels);

$faceCascade = new CvCascade();
$faceCascade->load("haarcascade_frontalface_default.xml");

$camera = new CvCapture();
$frame = $camera->queryFrame();
$gray = $frame->convertColor(CV_BGR2GRAY);
$faces = $faceCascade->detectMultiScale($gray);

foreach ($faces as $face) {
    $recognizedLabel = $lbph->predict($gray);
  
    if ($recognizedLabel == 0) {
        $label = "Tom";
    } else {
        $label = "Jane";
    }
  
    $frame->rectangle($face->x, $face->y, $face->x + $face->width, $face->y + $face->height);
    $frame->putText($label, new CvPoint($face->x, $face->y - 20), new CvFont(CV_FONT_HERSHEY_SIMPLEX, 1, 1));
}

$frame->showImage();
?>

In the above code, we use cv2.LBPHFaceRecognizer() to train the face model, and use the trained model to recognize real-time camera images.

Conclusion:
By using PHP to control the camera and combining it with the OpenCV library for face detection and recognition, we can implement a real-time face recognition system and improve security. This method can be applied to various environments, such as company access control, monitoring systems, etc. Through continuous learning and practice, we can further improve this system and make it more stable and accurate.

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