Computer vision is a technology that uses algorithms to replicate the abilities of human visual perception. It is one of the most prominent fields of Artificial Intelligence, as it has become useful in various aspects of technology. Face recognition is one of the most sought-after applications of Computer Vision in today's society, as it has been incorporated in many daily activities, including unlocking mobile phones.
Course Description
The course, named Computer Vision: Face Recognition Quick Starter in Python, is an updated version of the Computer Vision series that delves deeper into Python Deep Learning based Face Detection, Face Recognition, Emotion, Gender, and Age Classification using all popular models including Haar Cascade, HOG, SSD, MMOD, MTCNN, EigenFace, FisherFace, VGGFace, FaceNet, OpenFace, DeepFace. The course has a 4.52189 aggregate course rating, with 336 course reviews received to date.
The Headlines
The headline for this course is Python Deep Learning based Face Detection, Recognition, Emotion , Gender and Age Classification using all popular models.
Course Objective
The objective of the Computer Vision: Face Recognition Quick Starter in Python course is to provide a quick start for anyone who wishes to dive deep into face recognition using Python without having to deal with all the complexities and mathematics that come with typical Deep Learning processes. The course uses a python library called face-recognition, which uses simple classes and methods to get the face recognition implemented with ease. OpenCV, Dlib, and Pillow are also used as supporting libraries.
Course Outline
Face Detection and Face Recognition
The course starts with an introductory theory session about Face Detection and Face Recognition technology. Face Detection will be used to extract one or more faces from an image or video and then compare them with the existing data to identify the people in that image. In contrast, Face Recognition will identify the faces already detected.
Preparing the Computer for Python Coding
After the introductory theory, the course will then move on to prepare the computer for python coding by downloading and installing the anaconda package. Then, the rest of the dependencies and libraries that are required, including the dlib, face-recognition, opencv, etc., will be installed. Afterwards, a small program will be run to check if everything is installed fine.
Basic Python Programming Skills
Most of the learners may not have a Python-based programming background. As such, the next few sessions and examples will assist them in gaining basic python programming skills to proceed with sessions included in this course. The topics include Python assignment, flow-control, functions, and data structures.
Face Detection using Popular Algorithms
Then, the course moves on to explaining the basics and working of face detectors which will detect human faces from a given media and then the python code to detect the faces from a given image will be tried. The final objective is to extract the faces separately as images. The course then moves on to face detection from a video, where we will be streaming the real-time live video from the computer's webcam and will try to detect faces from it. Furthermore, the course explains how to customize the face detection programs to blur the detected faces dynamically from the webcam video stream.
Facial Expression, Gender and Age Recognition
We will be exploring facial expression recognition using pre-trained deep learning models to identify the facial emotions from the real-time webcam video as well as static images. The classes will then move towards Age and Gender Prediction using pre-trained deep learning models to identify the Age and Gender from the real-time webcam video as well as static images.
Traditional and Modern Face Recognition Methods
After mastering face detection, face alignment, and face feature extraction verification, the course will proceed with face recognition using traditional and modern methods. Starting with the traditional methods, the techniques of Eigenface Fisherface and LBPH, the Local Binary Pattern Histogram, will be introduced.
VGGNet model for Face Recognition
We will then move on to the VGGNet model for face recognition called VGGface. An introduction to VGG face will also be given. Later, the course will implement VGGface face verification for images, videos, as well as real-time streams.
Deep Learning Face Recognition Models
In addition to the traditional methods, deep learning face recognition models will also be explored, beginning with an introduction to FaceNet, OpenFace, and DeepFace Models. The course will then move on to utilize a popular easy-to-use open-source python face recognition framework called deepface to implement the rest of popular deep learning techniques.
Course Outcome
After completing this course, the learners will have the ability to:
- Gain knowledge about Face Detection and Face Recognition technology
- Prepare their computer for python coding
- Learn Basic Python Programming Skills including Python assignments, flow control, functions, and data structures.
- Detect faces from an image, video, and real-time webcam stream
- Complete facial expression recognition, Age, and Gender Prediction using pre-trained deep learning models.
- Learn traditional and modern face recognition methods, including VGGnet
- Implement Deep Learning Face Recognition Models using OpenFace, DeepFace, and FaceNet
- Create their own custom face make-up for the face image
- Gather requisite skills to use and deploy face recognition in a practical setting
- Grasp the concepts relating to the face recognition pipeline and land mark points used for face detection
Course Prospects
Computer Vision: Face Recognition Quick Starter in Python has received an aggregate course rating of 4.52189 and has had 336 course reviews as of the time of writing. The course is one of the best in the field of Computer Vision and Face Recognition, making it ideal for anyone who wishes to gain knowledge in the field.
This course covers a vast and broad range of aspects and concepts that are relevant to the field of Computer Vision. It will benefit students who are looking to master traditional and modern face recognition methods using deep learning algorithms.
Course Experience
The Computer Vision: Face Recognition Quick Starter in Python course offers an engaging and informative educational experience. The course is designed to provide a hands-on approach to learning, allowing learners to dive deeper into the real-life implementation of the technology.
The course's instructor is an experienced course review content writer with over 20 years of experience. He is an expert in the field of Computer Vision and has a deep understanding of the course content. The content is broken down into simple language with easy-to-understand examples that are crucial to mastering the concepts.
The course is available online, which means learners can learn at their own pace and according to their own style. The learner is given access to the course materials, including downloadable code, images, libraries, and other resources, for free. The access to downloadable resources makes it easier for students to replicate the exercises conducted during the course in their own personal projects.
Course Completion
Upon completion of the Computer Vision: Face Recognition Quick Starter in Python course, the learners will receive a course completion certificate, which will add value to their portfolio and will affirm their knowledge and expertise in the field of Computer Vision and Face Recognition.
The course is ideal for anyone who wishes to dive deeper into Face Recognition, and with the skills gained through this course, the learners can utilize it in a practical application setting
