Real-Time Face Recognition with PHP and OpenCV

With the advancement of technology, facial recognition is increasingly used in security domains; this article explains how to use PHP to control a camera for real-time face recognition by leveraging the OpenCV library.

The introduction outlines the importance of safety and the efficiency of facial recognition, emphasizing the goal of improving security through a PHP‑based solution.

Environment setup: ensure PHP and the OpenCV extension are installed, verify loaded extensions with php -m, and download the OpenCV library so it can be referenced in the project.

Controlling the camera with PHP is achieved by invoking system commands. Example 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);
?>

This script uses exec to call raspistill for capturing an image and then displays it on a web page.

Face detection with OpenCV involves loading the Haar cascade classifier and processing frames from the camera. Example code:

<?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();
?>

This code detects faces in each captured frame and draws rectangles around them.

Combining detection with recognition uses the LBPH algorithm to train a model and predict identities. Example code:

<?php
$images = glob("train_images/*.jpg");
$labels = [0, 0, 1, 1]; // training labels

$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();
?>

The script trains an LBPH recognizer, detects faces, predicts the label, and annotates the video stream with the identified name.

Conclusion: By integrating PHP camera control with OpenCV’s detection and LBPH recognition, a functional real‑time facial recognition system can be built, suitable for access control, surveillance, and other security scenarios, with room for further optimization and stability improvements.