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Welcome to the "Complete Face Recognition Attendance System Using KNN" course! In this hands-on project-based course, you will learn how to build a comprehensive face recognition attendance system using the K-Nearest Neighbors (KNN)
In this hands-on course, you’ll learn how to create a powerful Face Recognition Attendance system that detects and marks attendance automatically using live webcam input. Whether you're a beginner or an enthusiast in computer vision, this course will help you master every step of the Face Recognition Attendance workflow.
We’ll start with face detection, proceed to face encoding and recognition, and then build the logic to automate Face Recognition Attendance using Python and OpenCV. You’ll also learn how to store attendance records securely in CSV or database files as part of your Face Recognition Attendance project.
By the end of the course, you’ll have built a complete Face Recognition Attendance system, ideal for classrooms, offices, or security use cases. This practical project will be a great addition to your portfolio and skill set.
Introduction to Face Recognition Technology:
Understand the basics of face recognition technology and its applications.
Explore different face recognition algorithms and their strengths and weaknesses.
Setting Up the Development Environment:
Install necessary libraries and dependencies, including OpenCV and scikit-learn, for face recognition and KNN algorithm implementation.
Set up the development environment and create a new project directory.
Data Collection and Preprocessing:
Collect face images from various sources and individuals to create a dataset for training.
Preprocess the face images by resizing, cropping, and normalizing them to ensure consistency and accuracy in recognition.
Feature Extraction and Representation:
Extract facial features from the preprocessed images using techniques like Principal Component Analysis (PCA) or Local Binary Patterns (LBP).
Represent the facial features as feature vectors suitable for input to the KNN algorithm.
Implementing the KNN Algorithm:
Understand the principles of the K-Nearest Neighbors (KNN) algorithm for classification.
Implement the KNN algorithm using Python and scikit-learn library for face recognition.
Training and Evaluation:
Split the dataset into training and testing sets and train the KNN classifier on the training data.
Evaluate the performance of the face recognition system using metrics such as accuracy, precision, and recall.
Integration with Attendance System:
Develop a user-friendly interface for the attendance system using graphical user interface (GUI) tools like Tkinter or PyQt.
Integrate the trained KNN classifier into the attendance system to recognize faces and record attendance.
Testing and Deployment:
Test the face recognition attendance system with real-world data and scenarios to ensure functionality and accuracy.
Deploy the attendance system for practical use in educational institutions, businesses, or other organizations.
Introduction To Course
DATASET MODULE CLASS 1 : IMPORT PACKAGES
DATASET MODULE CLASS 2 : IMPORT DATASET & OPENCV
DATASET MODULE CLASS 3 : OUTPUT & EXPLANATION
ATTENDANCE MODULE CLASS 1 : IMPORT PACKAGES
ATTENDANCE MODULE CLASS 2 : IMPORT DATASET & OPENCV
ATTENDANCE MODULE CLASS 3 : TRAIN DATASET USING KNN
ATTENDANCE MODULE CLASS 4 : OUTPUT & CONCLUSION
The following course is fully online, and hence there is no need for any physical classroom session. The lectures and assignments can be accessed anytime and anywhere through a smart web or mobile device.
Anyone can choose a preferred course and start immediately without any delay.
As this is a purely online course program, you can choose to learn at any time of the day and for as much time as you want. Though we follow a well-established structure and schedule, we recommend a routine for you as well. But it finally depends on you, as you have to learn.
If you have completed the course, you would be able to have lifetime access to it for future reference too.
Yes, you can access and download the content of the course for the duration. And even have lifetime access to it for any further reference.
All the software/tools that you need for the course would be shared with you during the training as and when you need them.
No, only a soft copy of the certificate will be awarded, which can be downloaded and printed, if required.
You can try to make the payment through a different card or account (maybe a friend or family). If the problem persists, email us at info@easyshiksha.com
Due to some technical faults, this can happen. In such a case the amount deducted will be transferred to the bank account in the next 7-10 working days. Normally the bank takes this much time to credit the amount back into your account.
At times, there may be a slight delay in your payment reflecting on your EasyShiksha dashboard. However, if the problem is taking longer than 30 minutes, please let us know by writing to us at info@easyshiksha.com from your registered email id, and attach the screenshot of the payment receipt or transaction history. Soon after verification from the backend, we will update the payment status.
If you have enrolled, and are facing any technical problem then you can request a refund. But once the certificate has been generated, we shall not refund that.
Yes! You surely can. To begin this, just click the course of your interest and fill in the details to enrol. You are ready to learn, once the payment is made. For the same, you earn a certificate too.
Please contact us at: info@easyshiksha.com
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