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Christ Raharja

Welcome to Computer Vision Bootcamp: Building Face Recognition with OpenCV course. This is a comprehensive projects based course where you will learn step by step on how to build a face recognition system, emotion detection system, age detection system, and eye tracking system using OpenCV. This course is a perfect combination between computer vision and image processing. This course will equip you with essential skills in feature extraction and pattern recognition, enabling you to develop complex facial recognition systems. In the introduction session, you will learn about the basic fundamentals of facial recognition, such as getting to know its use cases, technologies that will be used, and its limitations. Then, in the next session, you will learn how facial recognition technology works. This section will cover data collection, preprocessing, model training, feature extraction, and face matching. Afterward, we will start the project section, in the first section, we will build facial recognition for the identity verification system. Firstly, we are going to prepare the training data. It consists of several information like name, photo, age, and gender. Then, the data will be used to train the facial recognition model and it will enable the model to verify if you are the same person as the stored data based on the facial structure analysis. In addition, the model will also be able to make a decision, if the face matched then the model will print, access granted, however if the face did not match, then, the model will print, access denied. Meanwhile, in the second project section, we will build an emotion detection system using OpenCV. In this case, we will obtain a dataset from Kaggle and use that data to train the model to be able to detect emotion and facial expression. Then, in the third project section, we will build an age detection system using OpenCV. We will use a dataset containing photos of people from various ages and use it to train the model to predict someone’s age based on their facial structure. In the fourth project section, we will build an eye tracking system that can be utilized to analyze movements of a student's eyes to monitor their gaze patterns during an online exam. In addition, the model will also be able to notify the teacher if suspicious behavior is detected. This technology can potentially help teachers and college professors to maintain the academic integrity in their online class.

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Welcome to Computer Vision Bootcamp: Building Face Recognition with OpenCV course. This is a comprehensive projects based course where you will learn step by step on how to build a face recognition system, emotion detection system, age detection system, and eye tracking system using OpenCV. This course is a perfect combination between computer vision and image processing. This course will equip you with essential skills in feature extraction and pattern recognition, enabling you to develop complex facial recognition systems. In the introduction session, you will learn about the basic fundamentals of facial recognition, such as getting to know its use cases, technologies that will be used, and its limitations. Then, in the next session, you will learn how facial recognition technology works. This section will cover data collection, preprocessing, model training, feature extraction, and face matching. Afterward, we will start the project section, in the first section, we will build facial recognition for the identity verification system. Firstly, we are going to prepare the training data. It consists of several information like name, photo, age, and gender. Then, the data will be used to train the facial recognition model and it will enable the model to verify if you are the same person as the stored data based on the facial structure analysis. In addition, the model will also be able to make a decision, if the face matched then the model will print, access granted, however if the face did not match, then, the model will print, access denied. Meanwhile, in the second project section, we will build an emotion detection system using OpenCV. In this case, we will obtain a dataset from Kaggle and use that data to train the model to be able to detect emotion and facial expression. Then, in the third project section, we will build an age detection system using OpenCV. We will use a dataset containing photos of people from various ages and use it to train the model to predict someone’s age based on their facial structure. In the fourth project section, we will build an eye tracking system that can be utilized to analyze movements of a student's eyes to monitor their gaze patterns during an online exam. In addition, the model will also be able to notify the teacher if suspicious behavior is detected. This technology can potentially help teachers and college professors to maintain the academic integrity in their online class.

First of all, before getting into the course, we need to ask ourselves this question: why should we build facial recognition systems? Well, here is my answer: facial recognition systems play a pivotal role in supporting security measures across various sectors, including banking, government, and corporate environments. By accurately identifying individuals based on unique facial features, these systems provide a robust means of access control, ensuring only authorized personnel can gain entry to restricted areas or sensitive information. Moreover, in the digital world, facial recognition serves as a powerful tool for identity verification and authentication, safeguarding online accounts, transactions, and personal data from unauthorized access or fraudulent activities. Its ability to verify identity in real time offers unparalleled security and convenience, mitigating risks associated with traditional authentication methods like passwords or PINs, which are susceptible to theft or exploitation. As the threat landscape continues to evolve, the adoption of facial recognition technology remains paramount in safeguarding assets, maintaining trust, and upholding the integrity of digital ecosystems

Below are things that you can expect to learn from this course:

  • Learn the basics fundamentals of facial recognition technology, such as getting to know its use cases, technologies used, and limitations

  • Learn how facial recognition systems work. This section will cover data collection, data preprocessing, model training, scanning face, image preprocessing, face features extraction, and access management

  • Learn how to activate camera using OpenCV

  • Learn how to build facial recognition system using OpenCV

  • Learn how to create training data for facial recognition system

  • Learn how to create function to load images from training data folder

  • Learn how to create access management and identity verification system

  • Learn how to draw rectangle around face

  • Learn how to train emotion detection model using Keras

  • Learn how to build emotion detection system using OpenCV

  • Learn how to build age detection system using OpenCV

  • Learn how to build eye tracking system using OpenCV

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What's inside

Learning objectives

  • Learn how to build facial recognition system using opencv
  • Learn how to build emotion detection system using opencv
  • Learn how to build age detection system using opencv
  • Learn how to build eye tracking system using opencv
  • Learn how to create access management and identity verification system
  • Learn how to create training data for facial recognition system
  • Learn how to create function to load images from training data folder
  • Learn how to activate camera using opencv
  • Learn how to train emotion detection model using keras
  • Learn how facial recognition systems work. this section will cover data collection, data preprocessing, model training, scanning face, and feature extraction
  • Learn the basics fundamentals of facial recognition technology, such as getting to know its use cases, technologies used, and limitations
  • Learn how to draw rectangle around face
  • Show more
  • Show less

Syllabus

Getting to know the general overview of the course and things that you can expect to learn from this course
Introduction
Table of Contents
Whom This Course is Intended for?
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Getting to know the programming language, libraries, IDE, and datasets that will be used
Tools, IDE, and Datasets
Learning the basics fundamentals of facial recognition technology, such as getting to know its use cases, technologies used, and limitations
Introduction to Facial Recognition Technology
Learning how facial recognition systems work. This section will cover data collection, data preprocessing, model training, scanning face, image preprocessing, and features extraction
How Facial Recognition System Works?
Learning how to install OpenCV, Numpy, and other Python packages
Installing OpenCV & Numpy
Learning how to activate camera using OpenCV
Activating Camera Using OpenCV
Learning how to create training data for facial recognition system
Preparing Training Data for Facial Recognition Model
Learning how to build facial recognition system using OpenCV
Creating Function to Load Images From Training Data Folder
Creating Access Management & Identity Verification System
Drawing Rectangles Around Face
Conducting testing on facial recognition system and making sure it is working well
Testing Facial Recognition System
Learning how to build emotion detection system using OpenCV
Finding & Downloading Emotion Dataset From Kaggle
Training Emotion Detection Model with Keras
Building Emotion Detection System with OpenCV
Conducting testing on emotion detection system and making sure it is working well
Testing Emotion Detection System
Learning how to build age detection system using OpenCV
Building Age Detection System with OpenCV
Conducting testing on age detection system and making sure it is working well
Testing Age Detection System
Learning how to build eye tracking system using OpenCV
Building Eye Tracking System with OpenCV
Conducting testing on eye tracking system and making sure it is working well
Testing Eye Tracking System
Summarising all things that we've learnt in this course and sharing few tips and tricks on how to improve the accuracy and performance of the model
Conclusion & Summary

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Builds robust skills in computer vision and image processing, which are core skills for various industries
Provides hands-on experience in developing facial recognition, emotion detection, age detection, and eye tracking systems using OpenCV
Specifically tailored for beginners interested in exploring computer vision and facial recognition
Covers essential concepts, including data collection, preprocessing, model training, and feature extraction in facial recognition
Suitable for learners in fields where facial recognition and computer vision applications are relevant, such as security, identity verification, and data analysis

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Computer Vision Bootcamp: Build Face Recognition with OpenCV with these activities:
Brush up on Linear Algebra
Review the basics of linear algebra to strengthen your foundational understanding and prepare for the upcoming course materials.
Show steps
  • Review matrix operations, including addition, subtraction, multiplication, and transposition.
  • Practice solving systems of linear equations using Gaussian elimination.
  • Familiarize yourself with vector spaces, subspaces, and linear independence.
Organize and Review Course Resources
Keep your notes, assignments, quizzes, and exams organized to facilitate efficient review and retention of course content.
Show steps
  • Create a system for categorizing and organizing your course materials.
  • Review your notes and assignments regularly to reinforce your understanding.
  • Solve practice problems or attempt quizzes to test your knowledge.
Join a Study Group or Online Forum
Connect with other students enrolled in the course through study groups or online forums to discuss concepts, share knowledge, and support each other's learning.
Show steps
  • Search for existing study groups or forums related to the course topic.
  • Join a group or forum that aligns with your learning style and schedule.
  • Participate actively in discussions, ask questions, and share your insights.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore OpenCV Documentation and Tutorials
Explore the official OpenCV documentation and tutorials to gain a deeper understanding of the library's capabilities and how to apply it effectively.
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  • Visit the OpenCV website and browse the documentation for the modules relevant to the course.
  • Work through the tutorials provided by OpenCV, experimenting with the code examples.
  • Search for additional tutorials and resources online to supplement your learning.
Complete OpenCV Exercises
Engage in hands-on practice by completing exercises that utilize OpenCV functions to solve specific computer vision tasks.
Show steps
  • Find and download a collection of OpenCV exercises or problems online.
  • Implement the solutions to the exercises using OpenCV functions.
  • Test your solutions and debug any errors.
Build a Resource Repository for OpenCV
Contribute to the community by compiling a collection of valuable resources, tools, and tutorials related to OpenCV.
Show steps
  • Gather and curate a collection of useful OpenCV resources from various sources.
  • Organize the resources into a logical structure, such as by topic or application.
  • Create a platform or website to host and share the repository with others.
Develop a Facial Recognition Application
Apply your knowledge by creating a functional facial recognition application that can perform tasks such as face detection, recognition, and authentication.
Show steps
  • Design the architecture and user interface for your application.
  • Implement the core functionality using OpenCV and other relevant libraries.
  • Test and refine your application to ensure accuracy and usability.
Assist Newcomers to OpenCV
Share your knowledge and support fellow learners by answering questions, providing guidance, and offering encouragement to those who are new to OpenCV or computer vision.
Show steps
  • Identify platforms or forums where you can connect with beginners in OpenCV.
  • Answer questions, share resources, and provide guidance to those seeking assistance.
  • Create or contribute to tutorials or documentation that can benefit newcomers.

Career center

Learners who complete Computer Vision Bootcamp: Build Face Recognition with OpenCV will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers design, develop, and implement machine learning models to solve real-world problems. This course on facial recognition provides a solid foundation in machine learning concepts such as supervised learning, unsupervised learning, and neural networks. You will learn how to build and train machine learning models for facial recognition, which is a critical skill for Machine Learning Engineers.
Artificial Intelligence Researcher
Artificial Intelligence Researchers are involved in the development of new and innovative AI technologies. This course on facial recognition provides a comprehensive understanding of AI concepts such as machine learning, deep learning, and computer vision. By taking this course, you will gain the knowledge and skills necessary to conduct research in facial recognition and contribute to the advancement of AI.
Data Scientist
Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course on facial recognition provides a comprehensive understanding of data science concepts such as data mining, machine learning, and statistical modeling. By taking this course, you will gain the skills necessary to build and deploy facial recognition models, which are in high demand across various industries.
Software Engineer
Software Engineers design, develop, and maintain software applications. This course on facial recognition provides a comprehensive understanding of software development principles and best practices. You will learn how to design and implement facial recognition algorithms and integrate them into software applications, which is a highly sought-after skill in the software industry.
Computer Scientist
Computer Scientists are involved in the design, development, and implementation of computer systems and software applications. This course on facial recognition covers in-demand topics such as feature extraction, machine learning, and pattern recognition, which are all fundamental concepts in computer science. By taking this course, you will gain a strong foundation in these key areas and enhance your skills as a Computer Scientist.
Systems Analyst
Systems Analysts design, develop, and implement computer systems and software applications. This course on facial recognition provides a comprehensive understanding of systems analysis and design principles. You will learn how to design and implement facial recognition systems that are efficient, reliable, and secure, which is a critical skill for Systems Analysts working in various industries.
Data Engineer
Data Engineers build and maintain data pipelines and infrastructure to support data-intensive applications. This course on facial recognition provides a comprehensive understanding of data engineering concepts such as data ingestion, transformation, and storage. By taking this course, you will gain the skills necessary to design and implement robust data engineering solutions that are essential for supporting facial recognition applications.
Web Developer
Web Developers design, develop, and maintain websites and web applications. This course on facial recognition provides a comprehensive understanding of web development technologies and best practices. You will learn how to integrate facial recognition functionality into websites and web applications, which is a highly sought-after skill in the web development industry.
Forensic Scientist
Forensic Scientists analyze and interpret evidence from crime scenes and other sources to help law enforcement and legal professionals solve crimes. This course on facial recognition provides a comprehensive understanding of image and video analysis, feature extraction, and facial identification, which are essential skills for Forensic Scientists. By taking this course, you will enhance your expertise in identifying and analyzing facial evidence, assisting in criminal investigations.
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to extract meaningful insights. This course on facial recognition provides a solid foundation in data analysis techniques, including data collection, preprocessing, and visualization. You will learn how to extract valuable information from images and videos, which is a highly sought-after skill in the field of data analysis.
Visual Effects Artist
Visual Effects Artists create realistic images and animations for movies, television shows, and video games. This course on facial recognition provides a comprehensive understanding of image and video processing techniques. You will learn how to use facial recognition technology to create realistic facial animations and visual effects, which is a valuable skill for Visual Effects Artists working in the entertainment industry.
Statistician
Statisticians collect, analyze, interpret, and present data to help businesses and organizations make informed decisions. This course on facial recognition provides a solid foundation in statistical concepts such as probability, inference, and regression. You will learn how to apply statistical methods to facial recognition problems, which is an essential skill for Statisticians working in fields such as biometrics and computer vision.
UX Designer
UX Designers are responsible for designing the user interface and user experience of products and services. This course on facial recognition provides a comprehensive understanding of user-centered design principles and best practices. You will learn how to design facial recognition interfaces that are intuitive, accessible, and enjoyable to use, which is a highly sought-after skill in the UX design industry.
Biomedical Engineer
Biomedical Engineers are focused on applying engineering principles to biological and medical problems. As AI becomes more and more integrated with various medical technologies, from robotics to medical imaging, understanding the fundamentals of facial recognition will be increasingly relevant to this field. This course on facial recognition provides a comprehensive overview of the technology, including topics like face detection, feature extraction, and facial expression analysis, which are all essential components of many medical and healthcare technologies.
Product Manager
Product Managers are responsible for defining, developing, and launching products that meet customer needs. This course on facial recognition provides a comprehensive understanding of user research, product development, and market analysis. By taking this course, you will learn how to identify customer needs and develop facial recognition products that are both innovative and valuable.

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Computer Vision Bootcamp: Build Face Recognition with OpenCV.
Provides a practical guide to deep learning for computer vision. It covers the latest deep learning techniques and architectures, and it provides hands-on exercises and projects to help you learn how to apply deep learning to computer vision tasks.
Provides a comprehensive overview of computer vision algorithms and their applications. It valuable resource for anyone interested in learning more about the field of computer vision.
Provides a comprehensive and up-to-date introduction to computer vision, covering a wide range of topics from image formation and analysis to object recognition and scene understanding.
Provides a practical guide to using OpenCV for computer vision tasks, such as image processing and object detection. It suitable companion for the section on installing OpenCV in the provided course.
Provides a comprehensive overview of the computer vision techniques that are used in autonomous vehicles, such as object detection, tracking, and scene understanding. It suitable companion for the section describing how to build an eye tracking system using OpenCV.
Provides a comprehensive overview of the machine learning techniques that are used in computer vision, such as supervised learning, unsupervised learning, and reinforcement learning. It useful companion to the sections of the course that describe training the emotion detection, age detection, and eye tracking systems.
Provides a comprehensive overview of the computer vision techniques that are used in medical imaging, such as image segmentation, registration, and visualization. It suitable companion for the book "Facial Emotion Recognition using OpenCV"

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