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Posted 2 Feb 2021

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Real-Time Face Tracking in the Browser with TensorFlow.js

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2 Feb 2021CPOL4 min read
In this series, we’re going to show you how to create Snapchat-style filters in the browser using Tensorflow.js and face tracking.
Here we will learn how to use AI models to detect the shape of faces.

Introduction

Apps like Snapchat offer an amazing variety of face filters and lenses that let you overlay interesting things on your photos and videos. If you’ve ever given yourself virtual dog ears or a party hat, you know how much fun it can be!

Have you wondered how you’d create these kinds of filters from scratch? Well, now’s your chance to learn, all within your web browser! In this series, we’re going to see how to create Snapchat-style filters in the browser, train an AI model to understand facial expressions, and do even more using Tensorflow.js and face tracking.

Image 1

You are welcome to download the demo of this project. You may need to enable WebGL in your web browser for performance. You can also download the code and files for this series.

We are assuming that you are familiar with JavaScript and HTML and have at least a basic understanding of neural networks. If you are new to TensorFlow.js, we recommend that you check out this guide: Getting Started with Deep Learning in Your Browser Using TensorFlow.js.

If you would like to see more of what is possible in the web browser withTensorFlow.js, check out these AI series: Computer Vision with TensorFlow.js and Chatbots using TensorFlow.js.

The first step to creating a face filter from scratch is to detect and locate faces in images, so we can start here.

Face tracking can be done with TensorFlow.js and the Face Landmarks Detection model, which can get us 486 different key points, in 3D, for each face inside an image or video frame, within a couple of milliseconds. What makes this especially great is that the model can run inside a web page, so that you can track faces on mobile devices too, using the same code.

Let’s set up a project to load the model and run face tracking on a webcam video feed.

Starting Point

Here is a starter template of the web page we will use for face tracking.

This template includes:

  • The TensorFlow.js libraries required for this project
  • A reference face mesh index set in triangles.js (included in the project code)
  • A canvas element for the rendered output
  • A hidden video element for the webcam
  • A status text element and the setText utility function
  • Canvas drawLine and drawTriangle utility functions
HTML
<html>
    <head>
        <title>Real-Time Face Tracking in the Browser with TensorFlow.js</title>
        <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.4.0/dist/tf.min.js"></script>
        <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/face-landmarks-detection@0.0.1/dist/face-landmarks-detection.js"></script>
        <script src="web/triangles.js"></script>
    </head>
    <body>
        <canvas id="output"></canvas>
        <video id="webcam" playsinline style="
            visibility: hidden;
            width: auto;
            height: auto;
            ">
        </video>
        <h1 id="status">Loading...</h1>
        <script>
        function setText( text ) {
            document.getElementById( "status" ).innerText = text;
        }

        function drawLine( ctx, x1, y1, x2, y2 ) {
            ctx.beginPath();
            ctx.moveTo( x1, y1 );
            ctx.lineTo( x2, y2 );
            ctx.stroke();
        }

        function drawTriangle( ctx, x1, y1, x2, y2, x3, y3 ) {
            ctx.beginPath();
            ctx.moveTo( x1, y1 );
            ctx.lineTo( x2, y2 );
            ctx.lineTo( x3, y3 );
            ctx.lineTo( x1, y1 );
            ctx.stroke();
        }

        (async () => {
            // TODO: Add code here
        })();
        </script>
    </body>
</html>

Using the HTML5 Webcam API with TensorFlow.js

Starting the webcam is quite simple in JavaScript if you have a code snippet for it. Here is a utility function for you to start the webcam and request access from the user:

JavaScript
async function setupWebcam() {
    return new Promise( ( resolve, reject ) => {
        const webcamElement = document.getElementById( "webcam" );
        const navigatorAny = navigator;
        navigator.getUserMedia = navigator.getUserMedia ||
        navigatorAny.webkitGetUserMedia || navigatorAny.mozGetUserMedia ||
        navigatorAny.msGetUserMedia;
        if( navigator.getUserMedia ) {
            navigator.getUserMedia( { video: true },
                stream => {
                    webcamElement.srcObject = stream;
                    webcamElement.addEventListener( "loadeddata", resolve, false );
                },
            error => reject());
        }
        else {
            reject();
        }
    });
}

We can call this setupWebcam function in an async block at the bottom of our code and make it play the webcam video after it loads.

JavaScript
(async () => {
    await setupWebcam();
    const video = document.getElementById( "webcam" );
    video.play();
})();

Next, let’s set up the output canvas and prepare to draw lines and triangles for the bounding box and face wireframe.

The canvas context will be used to output the face tracking results, so we can save that globally outside of the async block. Note that we mirrored the webcam horizontally to behave more naturally like a real mirror.

JavaScript
let output = null;

(async () => {
    await setupWebcam();
    const video = document.getElementById( "webcam" );
    video.play();
    let videoWidth = video.videoWidth;
    let videoHeight = video.videoHeight;
    video.width = videoWidth;
    video.height = videoHeight;

    let canvas = document.getElementById( "output" );
    canvas.width = video.width;
    canvas.height = video.height;

    output = canvas.getContext( "2d" );
    output.translate( canvas.width, 0 );
    output.scale( -1, 1 ); // Mirror cam
    output.fillStyle = "#fdffb6";
    output.strokeStyle = "#fdffb6";
    output.lineWidth = 2;
})();

Let’s Track Some Faces

Now we’re ready! All we need is to load the TensorFlow Face Landmarks Detection model and run it on our webcam frames to show the results.

First, we need a global model variable to store the loaded model:

JavaScript
let model = null;

Then we can load the model at the end of the async block and set the status text to indicate that our face tracking app is ready:

JavaScript
// Load Face Landmarks Detection
model = await faceLandmarksDetection.load(
    faceLandmarksDetection.SupportedPackages.mediapipeFacemesh
);

setText( "Loaded!" );

Now let’s create a function called trackFace that takes the webcam video frames, runs the face tracking model, copies the webcam image to the output canvas, and then draws a bounding box around the face and wireframe mesh triangles on top of the face.

JavaScript
async function trackFace() {
    const video = document.getElementById( "webcam" );
    const faces = await model.estimateFaces( {
        input: video,
        returnTensors: false,
        flipHorizontal: false,
    });
    output.drawImage(
        video,
        0, 0, video.width, video.height,
        0, 0, video.width, video.height
    );

    faces.forEach( face => {
        setText( `Face Tracking Confidence: ${face.faceInViewConfidence.toFixed( 3 )}` );

        // Draw the bounding box
        const x1 = face.boundingBox.topLeft[ 0 ];
        const y1 = face.boundingBox.topLeft[ 1 ];
        const x2 = face.boundingBox.bottomRight[ 0 ];
        const y2 = face.boundingBox.bottomRight[ 1 ];
        const bWidth = x2 - x1;
        const bHeight = y2 - y1;
        drawLine( output, x1, y1, x2, y1 );
        drawLine( output, x2, y1, x2, y2 );
        drawLine( output, x1, y2, x2, y2 );
        drawLine( output, x1, y1, x1, y2 );

        // Draw the face mesh
        const keypoints = face.scaledMesh;
        for( let i = 0; i < FaceTriangles.length / 3; i++ ) {
            let pointA = keypoints[ FaceTriangles[ i * 3 ] ];
            let pointB = keypoints[ FaceTriangles[ i * 3 + 1 ] ];
            let pointC = keypoints[ FaceTriangles[ i * 3 + 2 ] ];
            drawTriangle( output, pointA[ 0 ], pointA[ 1 ], pointB[ 0 ], pointB[ 1 ], pointC[ 0 ], pointC[ 1 ] );
        }
    });

    requestAnimationFrame( trackFace );
}

Finally, we can kick off the first frame for tracking by calling this function at the end of our async block:

JavaScript
(async () => {
    ...

    trackFace();
})();

Finish Line

The full code should look like this:

HTML
<html>
    <head>
        <title>Real-Time Face Tracking in the Browser with TensorFlow.js</title>
        <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.4.0/dist/tf.min.js"></script>
        <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/face-landmarks-detection@0.0.1/dist/face-landmarks-detection.js"></script>
        <script src="web/triangles.js"></script>
    </head>
    <body>
        <canvas id="output"></canvas>
        <video id="webcam" playsinline style="
            visibility: hidden;
            width: auto;
            height: auto;
            ">
        </video>
        <h1 id="status">Loading...</h1>
        <script>
        function setText( text ) {
            document.getElementById( "status" ).innerText = text;
        }

        function drawLine( ctx, x1, y1, x2, y2 ) {
            ctx.beginPath();
            ctx.moveTo( x1, y1 );
            ctx.lineTo( x2, y2 );
            ctx.stroke();
        }

        function drawTriangle( ctx, x1, y1, x2, y2, x3, y3 ) {
            ctx.beginPath();
            ctx.moveTo( x1, y1 );
            ctx.lineTo( x2, y2 );
            ctx.lineTo( x3, y3 );
            ctx.lineTo( x1, y1 );
            ctx.stroke();
        }

        let output = null;
        let model = null;

        async function setupWebcam() {
            return new Promise( ( resolve, reject ) => {
                const webcamElement = document.getElementById( "webcam" );
                const navigatorAny = navigator;
                navigator.getUserMedia = navigator.getUserMedia ||
                navigatorAny.webkitGetUserMedia || navigatorAny.mozGetUserMedia ||
                navigatorAny.msGetUserMedia;
                if( navigator.getUserMedia ) {
                    navigator.getUserMedia( { video: true },
                        stream => {
                            webcamElement.srcObject = stream;
                            webcamElement.addEventListener( "loadeddata", resolve, false );
                        },
                    error => reject());
                }
                else {
                    reject();
                }
            });
        }

        async function trackFace() {
            const video = document.getElementById( "webcam" );
            const faces = await model.estimateFaces( {
                input: video,
                returnTensors: false,
                flipHorizontal: false,
            });
            output.drawImage(
                video,
                0, 0, video.width, video.height,
                0, 0, video.width, video.height
            );

            faces.forEach( face => {
                setText( `Face Tracking Confidence: ${face.faceInViewConfidence.toFixed( 3 )}` );

                // Draw the bounding box
                const x1 = face.boundingBox.topLeft[ 0 ];
                const y1 = face.boundingBox.topLeft[ 1 ];
                const x2 = face.boundingBox.bottomRight[ 0 ];
                const y2 = face.boundingBox.bottomRight[ 1 ];
                const bWidth = x2 - x1;
                const bHeight = y2 - y1;
                drawLine( output, x1, y1, x2, y1 );
                drawLine( output, x2, y1, x2, y2 );
                drawLine( output, x1, y2, x2, y2 );
                drawLine( output, x1, y1, x1, y2 );

                // Draw the face mesh
                const keypoints = face.scaledMesh;
                for( let i = 0; i < FaceTriangles.length / 3; i++ ) {
                    let pointA = keypoints[ FaceTriangles[ i * 3 ] ];
                    let pointB = keypoints[ FaceTriangles[ i * 3 + 1 ] ];
                    let pointC = keypoints[ FaceTriangles[ i * 3 + 2 ] ];
                    drawTriangle( output, pointA[ 0 ], pointA[ 1 ], pointB[ 0 ], pointB[ 1 ], pointC[ 0 ], pointC[ 1 ] );
                }
            });

            requestAnimationFrame( trackFace );
        }

        (async () => {
            await setupWebcam();
            const video = document.getElementById( "webcam" );
            video.play();
            let videoWidth = video.videoWidth;
            let videoHeight = video.videoHeight;
            video.width = videoWidth;
            video.height = videoHeight;

            let canvas = document.getElementById( "output" );
            canvas.width = video.width;
            canvas.height = video.height;

            output = canvas.getContext( "2d" );
            output.translate( canvas.width, 0 );
            output.scale( -1, 1 ); // Mirror cam
            output.fillStyle = "#fdffb6";
            output.strokeStyle = "#fdffb6";
            output.lineWidth = 2;

            // Load Face Landmarks Detection
            model = await faceLandmarksDetection.load(
                faceLandmarksDetection.SupportedPackages.mediapipeFacemesh
            );

            setText( "Loaded!" );

            trackFace();
        })();
        </script>
    </body>
</html>

What’s Next? Can Face Tracking Do More?

By combining the TensorFlow Face Landmarks Detection model with the webcam video, we were able to track faces in real time right inside the browser. Our face tracking code also works on images, and the key points could tell us more than we might first expect. Maybe we should try it on a dataset of faces, like FER+ Facial Expression Recognition?

In the next article of this series, we’ll use Deep Learning on the tracked faces of the FER+ dataset and attempt to accurately predict a person’s emotion from facial points in the browser with TensorFlow.js.

License

This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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About the Author

Raphael Mun
United States United States
Raphael Mun is a tech entrepreneur and educator who has been developing software professionally for over 20 years. He currently runs Lemmino, Inc and teaches and entertains through his Instafluff livestreams on Twitch building open source projects with his community.

Comments and Discussions

 
GeneralMy vote of 5 Pin
raddevus2-Feb-21 8:07
mvaraddevus2-Feb-21 8:07 

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