Face recognition using JavaScript
Nikhil karkra
Posted on February 14, 2020
Face detection is one of the most common applications of Artificial Intelligence. The use of Facial detection has increased in the last couple of years.
Face-api.js has brought a JavaScript API for face detection and face recognition in the browser implemented on top of the tensorflow.js core API
In this tutorial, we will build the face recognition app that will work in the Browser. From the face, we will predict the Emotion, Gender, and age.
The output of this app will look as shown below.
Project Step
Step1 - Create a folder called face-recognition
Under the face-recognition folder create the following folder structure
All folders are self-explanatory except models. That I will cover in going forward.
Step2 - download the face-api.min.js
Download the face-api.min.js code from the following URL and paste it inside the js/face-api.min.js file.
https://raw.githubusercontent.com/karkranikhil/face-recognition-using-js/master/js/face-api.min.js
Step3 - download the modal files
Models are the trained data that we will use to detect the feature from the face.
Download the files from the following URL and placed them inside the models folder.
https://github.com/karkranikhil/face-recognition-using-js/tree/master/models
Step4 - Let's built the index.html file.
In index.html file we importing the style.css for styles, face-api.min.js for processing the model data and extracting the features and main.js where we will write our logic.
Inside the body tag we are creating a video tag to get the face, result-container for showing the emotion, gender, and age.
Place the below code inside the index.html file
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta http-equiv="X-UA-Compatible" content="ie=edge" />
<title>Face recognition App</title>
<link rel="stylesheet" href="css/style.css" />
</head>
<body>
<header>Face recognition in the browser using Javascript</header>
<div class="container">
<video id="video" height="500" width="500" autoplay muted></video>
</div>
<div class="result-container">
<div id="emotion">Emotion</div>
<div id="gender">Gender</div>
<div id="age">Age</div>
</div>
<script src="./js/face-api.min.js"></script>
<script src="./js/main.js"></script>
</body>
</html>
Step5 - Let's built the main.js file.
Inside the main.js file we are using promise.all to load the models to the face API. once the promise is resolved then we are calling the startVideo method that starts the streaming. Below are the methods used for this demo
faceapi.detectSingleFacemethod -detectSingleFaceutilize the SSD Mobilenet V1 Face Detector. You can specify the face detector by passing the corresponding options object. To detect the multiple faces replace thedetectSingleFacewithdetectAllFaceswithFaceLandmarks method- It is used for Detecting 68 Face Landmark PointswithFaceExpressions method- This method Detect all faces in an image + recognize facial expressions of each face and return the arraywithAgeAndGendermethod- This method Detect all faces in an image + estimate age and recognize gender of each face and return the array
Replace the following code to the main.js
const video = document.getElementById("video");
const isScreenSmall = window.matchMedia("(max-width: 700px)");
let predictedAges = [];
/****Loading the model ****/
Promise.all([
faceapi.nets.tinyFaceDetector.loadFromUri("/models"),
faceapi.nets.faceLandmark68Net.loadFromUri("/models"),
faceapi.nets.faceRecognitionNet.loadFromUri("/models"),
faceapi.nets.faceExpressionNet.loadFromUri("/models"),
faceapi.nets.ageGenderNet.loadFromUri("/models")
]).then(startVideo);
function startVideo() {
navigator.getUserMedia(
{ video: {} },
stream => (video.srcObject = stream),
err => console.error(err)
);
}
/****Fixing the video with based on size size ****/
function screenResize(isScreenSmall) {
if (isScreenSmall.matches) {
video.style.width = "320px";
} else {
video.style.width = "500px";
}
}
screenResize(isScreenSmall);
isScreenSmall.addListener(screenResize);
/****Event Listeiner for the video****/
video.addEventListener("playing", () => {
const canvas = faceapi.createCanvasFromMedia(video);
let container = document.querySelector(".container");
container.append(canvas);
const displaySize = { width: video.width, height: video.height };
faceapi.matchDimensions(canvas, displaySize);
setInterval(async () => {
const detections = await faceapi
.detectSingleFace(video, new faceapi.TinyFaceDetectorOptions())
.withFaceLandmarks()
.withFaceExpressions()
.withAgeAndGender();
const resizedDetections = faceapi.resizeResults(detections, displaySize);
canvas.getContext("2d").clearRect(0, 0, canvas.width, canvas.height);
/****Drawing the detection box and landmarkes on canvas****/
faceapi.draw.drawDetections(canvas, resizedDetections);
faceapi.draw.drawFaceLandmarks(canvas, resizedDetections);
/****Setting values to the DOM****/
if (resizedDetections && Object.keys(resizedDetections).length > 0) {
const age = resizedDetections.age;
const interpolatedAge = interpolateAgePredictions(age);
const gender = resizedDetections.gender;
const expressions = resizedDetections.expressions;
const maxValue = Math.max(...Object.values(expressions));
const emotion = Object.keys(expressions).filter(
item => expressions[item] === maxValue
);
document.getElementById("age").innerText = `Age - ${interpolatedAge}`;
document.getElementById("gender").innerText = `Gender - ${gender}`;
document.getElementById("emotion").innerText = `Emotion - ${emotion[0]}`;
}
}, 10);
});
function interpolateAgePredictions(age) {
predictedAges = [age].concat(predictedAges).slice(0, 30);
const avgPredictedAge =
predictedAges.reduce((total, a) => total + a) / predictedAges.length;
return avgPredictedAge;
}
Step6 - Let's Add the style to the app.
Replace the style.css with the following code.
body {
margin: 0;
padding: 0;
box-sizing: border-box;
height: 100vh;
background: #2f2f2f;
width: calc(100% - 33px);
}
canvas {
position: absolute;
}
.container {
display: flex;
width: 100%;
justify-content: center;
align-items: center;
}
.result-container {
display: flex;
width: 100%;
justify-content: center;
align-items: center;
flex-direction: column;
}
.result-container > div {
font-size: 1.3rem;
padding: 0.5rem;
margin: 5px 0;
color: white;
text-transform: capitalize;
}
#age {
background: #1e94be;
}
#emotion {
background: #8a1025;
}
#gender {
background: #62d8a5;
}
video {
width: 100%;
}
header {
background: #42a5f5;
color: white;
width: 100%;
font-size: 2rem;
padding: 1rem;
font-size: 2rem;
}
Step7 - Let's run the app by the live server or http-server
Once you run the app you will see the following output.
you can run the app deployed by me, using the following URL
https://face-recognition.karkranikhil.now.sh/
Reference
https://github.com/justadudewhohacks/face-api.js/
GITHUB - https://github.com/karkranikhil/face-recognition-using-js
Posted on February 14, 2020
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