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Face Recognition with Deep Learning Using Python Code
Learn face detection, face recognition and more with Python. Get hands-on experience with face recognition using Python libraries like OpenCV and Dlib.
Last updated
8/2024
Appropriate for all
Level4h 18m
Full lifetime access
Access on laptop, tablet and mobile devices
Certificate of completion
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Overview
Welcome to the course titled Face Recognition with Deep Learning Using Python Code which is part of my Computer Vision series. In this course, we will delve into the applications of face detection and face recognition, two fundamental aspects of Computer Vision.
Importance of Face Detection and Recognition
Course Objectives
Course Outline
The course will cover the following topics:
Introductory Theory Session
Understanding Face Detection and Recognition Technology
Setting Up the Development Environment
This course is designed to equip individuals with the essential knowledge and practical skills required to embark on the journey of face recognition using Python.
Introduction to Python Programming and Face Detection
Most of you may not have a background in Python-based programming. The upcoming sessions and examples will provide you with the fundamental Python programming skills necessary for the subsequent sessions in this course. The topics covered will include Python assignment, flow control, functions, and data structures.
Introduction to Face Detection
We will then introduce the basics and functionality of face detectors, which are capable of identifying human faces within a given media. We will implement Python code to detect faces within an image and extract them as separate images.
Face Detection in Video
Next, we will progress to face detection within a video. This will involve streaming real-time video from the computer's webcam and attempting to detect faces within it. Furthermore, we will outline rectangles around each face detected in the live video.
Dynamic Face Blurring
In a subsequent session, we will adapt the face detection program to dynamically blur the detected faces within the webcam video stream.
Facial Expression Recognition
Following that, we will explore facial expression recognition using a pre-trained deep learning model. This will enable us to identify facial emotions in real-time webcam video as well as static images.
Age and Gender Prediction
We will then delve into age and gender prediction using a pre-trained deep learning model. This will enable the identification of age and gender within real-time webcam video as well as static images.
Face Recognition
Finally, we will introduce the basics and functionality of face recognition, allowing for the identification of previously detected faces.
Python Code for Face Detection and Recognition
In the next session, we will try out Python code to identify the names of people and their faces from a given image. We will draw a rectangle around each face and display their names on it.
Face Recognition from a Video
Following the face detection exercise, we will move on to face recognition from a video. We will stream real-time live video from the computer's webcam and attempt to identify and name the faces in it. For each detected face, we will draw a rectangle and display the corresponding name in the live video.
Face Matching and Face Distance
During coding, it is often necessary to determine the level of similarity between two faces. To achieve this, we will use a parameter called face distance, which represents the magnitude of the match between two faces. We will later convert this face distance value into a face matching percentage using simple mathematics.
Customizing Face Landmark Points
In the following two sessions, we will learn how to customize the face landmark points used for face detection. By drawing lines to connect these points, we will be able to visualize the specific areas on the face that the computer uses for evaluation.
Creating Custom Face Make-Up
Taking the customization of landmark points to the next level, we will use these points to create a custom face make-up for the face image.
Conclusion and Course Completion Certificate
These are the topics currently covered in this quick course. Upon completion of the course, you will receive a course completion certificate, which will add value to your portfolio.
That's all for now. See you soon in the classroom. Happy learning and have a great time!
Who this course is for
Beginners or who wants to start with Python based Face Recognition.
Testimonials
What you'll learn
Requirements
A decent configuration computer and an enthusiasm to dive into the world of computer vision based Face Recognition.
Course Content
29 Sections 41 Lectures 4h 18m total length
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