Emotion Recognition via Camera Using PHP and Fer2013

Cameras are ubiquitous, and recognizing human emotions through them has become feasible with advances in artificial intelligence. This article demonstrates how to use PHP to operate a camera, capture video frames, and analyze facial expressions for emotion detection.

1. Preparation

First, install the PHP GD library for image processing and the V4L2 utilities for camera access on a Linux system.

sudo apt-get install php7.4-gd

sudo apt-get install v4l-utils

2. Obtaining the Camera Video Stream

Use PHP's shell_exec to run an FFmpeg command that captures a single frame from /dev/video0 and saves it as an image.

<?php
function getVideoStream() {
    $cmd = "ffmpeg -i /dev/video0 -vf fps=1 -s 1280x720 -f image2 -frames 1 /path/to/image.jpg";
    shell_exec($cmd);
    return "/path/to/image.jpg";
}
$videoStream = getVideoStream();
echo "<img src='$videoStream'>";
?>

3. Emotion Recognition and Facial Expression Analysis

Integrate the open‑source Fer2013 model by invoking a Python script from PHP. The script processes the captured image and returns the detected emotion.

4. Complete Example

The following code combines video capture and emotion detection, then displays both the image and the inferred emotion on a web page.

<?php
function getVideoStream() {
    $cmd = "ffmpeg -i /dev/video0 -vf fps=1 -s 1280x720 -f image2 -frames 1 /path/to/image.jpg";
    shell_exec($cmd);
    return "/path/to/image.jpg";
}
function getEmotion($imagePath) {
    $modelPath = "path/to/Fer2013/model.hdf5";
    $cmd = "python3 scripts/emotion_classification.py $modelPath $imagePath";
    $emotion = shell_exec($cmd);
    return $emotion;
}
$videoStream = getVideoStream();
$emotion = getEmotion($videoStream);

echo "<img src='$videoStream'>";
echo "Current emotion: $emotion";
?>

Running this example displays the live camera frame and the detected emotional state, providing a simple entry point for integrating camera‑based emotion recognition into PHP projects.

Conclusion

The guide shows how to set up the environment, capture video frames with PHP, and apply an AI model to recognize emotions, offering a foundational tutorial for developers interested in building emotion‑aware applications.