Create a real-time facial recognition-based attendance system using Python, OpenCV, and deep learning. From face enrollment to live recognition and GUI – learn to build it all step by step. Beginner-friendly. Certificate included.
By the end of this course, you'll have the skills to land your first computer vision job or freelance clients.
Discover how AI automates attendance by recognizing and verifying faces in real time.
Use Python with OpenCV and libraries like face_recognition or DeepFace for face detection and verification.
Build a real-time system that marks attendance by recognizing faces through a webcam.
Save attendance records with names, dates, and timestamps into a structured database.
Design a smooth GUI to show live face detection, attendance status, and user data.
Finish the course and receive a verified certificate of success.
Muhammad Yaqoob is the founder of Tentosoft Pvt Ltd and a seasoned Computer Vision expert. With 10+ years of experience and over 5,000+ students taught globally, he brings deep industry knowledge and a passion for practical, hands-on learning.
8 weeks of comprehensive training with 50+ lessons and 10+ hours of content
Introduces the concept of an AI-powered attendance system that uses facial recognition for automatic, real-time attendance tracking in schools, offices, and other organizations.
Course Overview and Features
Uses AI-based facial recognition to automate attendance
Enhances security and reduces proxy attendance
Provides accurate and tamper-proof attendance records
Setting up Python and its development tools is the foundation of building AI applications. This ensures the environment is ready to run and develop object detection models.
Installing Python
VS Code Setup for Python Development
Explains how to capture and register user facial data, which is essential for training and later identifying individuals during attendance checks.
Installing Required Packages (Dlib, OpenCV, etc.)
Capturing and Storing Facial Images
Captures facial images of each user for registration
Must be secure to protect user privacy and identity
Covers techniques for extracting unique facial embeddings from images using deep learning models, which are used to distinguish one face from another.
Extracting Face Embeddings and Identifying Landmark
Uses deep learning models to identify key facial landmarks
Utilizes pre-trained models like FaceNet or Dlib
Plays a vital role in accurate face matching
Demonstrates how to train a model using the enrolled facial data to enable accurate and fast recognition during real-time attendance checks.
Machine Learning-Based Training for Face Recognition
Uses enrolled face embeddings as training data
Employs models like KNN, SVM, or neural networks
Enables quick and accurate real-time face recognition
Shows how to implement real-time face detection and matching logic, automatically marking attendance as recognized faces appear in front of the camera.
Implementing Real-Time Face Recognition and Attendance Automation
Uses Tkinter to create an interactive GUI
Organizes layout using frames and grid positioning
Enhances usability with icons, colors, and fonts
Covers building a user-friendly interface using tools like Tkinter to visualize attendance logs and manage system controls interactively.
Designing and Integrating the Tkinter GUI
Uses Tkinter to develop a user-friendly interface
Enhances UX with intuitive icons, fonts, and colors
Is This Course Right for You?
Kickstart your AI journey with structured, hands-on learning.
Build a portfolio that recuriters can't ignore.
Add powerful AI/CV features to your apps and software.
Upskill for higher-paying, future-ready tech roles.
Build Smarter, more intelligent applications.
Transition into AI even with zero background.
One-time payment for lifetime access to all course materials and updates
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Highly recommended for small teams who seek to upgrade their time & perform.
₹ 6720 inclusive of GST ₹ 13999
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This certificate is given to Jacqueline Thompson for her achievement in the field of education and proves that she is competent in her field.
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