Using PHP and V4L2 to Capture Camera Stream and Perform Emotion Recognition with Fer2013

Camera devices are ubiquitous, and recognizing human emotions via camera is challenging, but AI advances make it possible. This article explains how to operate a camera with PHP on Linux using V4L2, capture video frames with FFmpeg, and apply the open‑source Fer2013 model for emotion classification.

1. Preparation

Install the PHP GD extension and V4L2 utilities:

sudo apt-get install php7.4-gd
sudo apt-get install v4l-utils

2. Obtaining 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, then display it with an <img> tag.

<?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

Integrate the Fer2013 emotion‑recognition library by calling a Python script from PHP. The script loads the pre‑trained model and returns the detected emotion.

<?php
function getEmotion($imagePath) {
    $modelPath = "path/to/Fer2013/model.hdf5";
    $cmd = "python3 scripts/emotion_classification.py $modelPath $imagePath";
    $emotion = shell_exec($cmd);
    return $emotion;
}
$emotion = getEmotion($videoStream);
echo "Current emotion: $emotion";
?>

Conclusion

The example demonstrates a basic end‑to‑end pipeline: capture a camera frame with PHP, process it through an AI model, and display both the image and the inferred emotion, providing a starter guide for camera‑based emotion detection projects.