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
- Face detection and recognition are pivotal applications in Computer Vision, enabling computers to extract and compare faces within images or videos against existing data to identify individuals.
- These techniques are extensively utilized by governments and organizations for surveillance and policing, as well as in everyday applications such as facial unlocking for mobile phones.
- This course aims to provide a quick introduction to face recognition using Python, bypassing the complexities and mathematical intricacies typically associated with deep learning processes.
- We will leverage the "face recognition" Python library, which offers straightforward classes and methods for seamless implementation of face recognition. Additionally, we will utilize supporting libraries such as OpenCV, Dlib, and Pillow for Python.
The course will cover the following topics:
Introductory Theory Session
Understanding Face Detection and Recognition Technology
Setting Up the Development Environment
- Configuring the Anaconda package for Python coding
- Installing necessary dependencies and libraries including Dlib, face-recognition, and OpenCV
- Verifying the installation through a simple program
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.
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.
- It was good learning about OpenCV Face recognition ~ D Tiwari
- Excellent course, in a few hours I learned what I was looking for ~ L Bentemuller
- Really simple and easy explanations to make out amazing things, keep it up ~ David
- For beginner like me, this course is awesome!! ~ Sapuan
- Till now, it's really interesting and helpful and the explanation is just fabulous. Looking forward to it ~ Prvind P
- Absolutely fantastic course ! Gets you into action quickly. Recommend ! ~ Z Sabakhtarishvili
- The intonation of the presentation is very clear. very details of explanations in the installation process ~ Illiana A
- Clear, easy to follow step by step instructions that deliver good and satisfactory outcomes that works! ~ SC Wong
- This instructor's lecture is excellent. Concise, organized, completed, runnable source codes is provided. This course is very helpful for you to understand face detection and recognition ~ Joseph F
- Very good and simple course to learn about OpenCV and basic python. The instructor will answer questions and help us if we get stuck ~ Ray C
- Very happy with this course. Despite I was expecting to get deeply into CNN's, the automatic methods and the introduction of entirely new libraries for me in terms of face recognition is really amazing. It helped me to understand the real-time part of the face analysis. Thank you very much ~ P Montoya
- This course enables me to build facial recognition and understand the fundamental concepts quickly! ~ William T
- It was very well explained, lucky to have found this amazing course. This was very precisely explained ~ Antara K
What you'll learn
- Face Detection from Images
- Face Detection from Realtime Videos
- Emotion Detection
- Age-Gender Prediction
- Face Recognition from Images
- Face Recognition from Realtime Videos
- Face Distance
- Face Landmarks Manipulation
- Face Makeup
- Also includes a Python basics refresher session.
A decent configuration computer and an enthusiasm to dive into the world of computer vision based Face Recognition.