1. Introduction

In this tutorial we will cover the basics of implementing face recognition in an HTML application using deep learning techniques. We'll be using JavaScript along with some libraries like face-api.js that simplifies face detection, face recognition and face landmark detection.

By the end of this tutorial, you will learn:

• Basic understanding of deep learning.
• How to use face-api.js library for face recognition.
• How to implement the face recognition in an HTML application.

Prerequisites:
Basic understanding of HTML, CSS and JavaScript is required. Prior knowledge of deep learning is beneficial but not mandatory.

2. Step-by-Step Guide

Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—to learn from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help optimize accuracy.

Face-api.js is a JavaScript API for face detection and face recognition in the browser implemented on top of the tensorflow.js core API, which implements a series of convolutional neural networks (CNN).

3. Code Examples

Example 1: Loading the model

First, let's load the pre-trained models.

const MODEL_URL = '/models';

await faceapi.loadSsdMobilenetv1Model(MODEL_URL)
await faceapi.loadFaceLandmarkModel(MODEL_URL)
await faceapi.loadFaceRecognitionModel(MODEL_URL)

• loadSsdMobilenetv1Model(MODEL_URL): This is used to load the SSD Mobilenet V1 Model for face detection.
• loadFaceLandmarkModel(MODEL_URL): This is used to load the Face Landmark Model to detect the facial landmarks.
• loadFaceRecognitionModel(MODEL_URL): This is used to load the Face Recognition Model to recognize the faces.

Example 2: Detecting the face

Now, let's detect the face from an image.

const input = document.getElementById('myImage')

const detections = await faceapi.detectAllFaces(input)

• detectAllFaces(input): This function is used to detect all the faces in the image.

Example 3: Drawing the detections

Finally, let's draw the detection result on the image.

const canvas = faceapi.createCanvasFromMedia(input)

faceapi.draw.drawDetections(canvas, detections)

• createCanvasFromMedia(input): This function is used to create a canvas from a media element.
• draw.drawDetections(canvas, detections): This function is used to draw the detections result on the canvas.

4. Summary

In this tutorial, we learned how to implement face recognition in an HTML application using deep learning. We discussed about deep learning and the use of face-api.js library for face recognition. We also saw how to load the model, detect the face and draw the detection result on the image.

To continue learning, you can explore more about deep learning and other libraries for face recognition. You can also try implementing this in real-time video instead of a static image.

5. Practice Exercises

Exercise 1: Try to implement the face recognition in real-time video.

Exercise 2: Try to recognize multiple faces in an image.

Exercise 3: Try to draw the facial landmarks on the face.

Solutions:

1. To implement the face recognition in real-time video, you need to get the video stream from the webcam and call the detectAllFaces function in an interval.

2. To recognize multiple faces in an image, you can use the detectAllFaces function which will return an array of all the faces detected in the image.

3. To draw the facial landmarks on the face, you can use the detectSingleFace function followed by withFaceLandmarks function and then use the draw.drawFaceLandmarks function to draw the landmarks on the canvas.