https://github.com/2000pawan/smart-face-recognition-system

👁️ Smart Face Recognition System using Machine Learning This is a GUI-based Smart Face Recognition System built using Python, Tkinter, OpenCV, and Machine Learning techniques. It can recognize faces in real-time webcam feeds capturing new training data via webcam.
https://github.com/2000pawan/smart-face-recognition-system

ai artificial-intelligence computer-vision face-recognition haar-cascade-classifier image-processing machine-learning opencv pillow python tkinter-gui

Last synced: about 2 months ago
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👁️ Smart Face Recognition System using Machine Learning This is a GUI-based Smart Face Recognition System built using Python, Tkinter, OpenCV, and Machine Learning techniques. It can recognize faces in real-time webcam feeds capturing new training data via webcam.

• Host: GitHub
• URL: https://github.com/2000pawan/smart-face-recognition-system
• Owner: 2000pawan
• License: mit
• Created: 2025-05-30T19:31:33.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-05-30T20:20:59.000Z (about 1 year ago)
• Last Synced: 2026-04-30T16:33:31.334Z (about 2 months ago)
• Topics: ai, artificial-intelligence, computer-vision, face-recognition, haar-cascade-classifier, image-processing, machine-learning, opencv, pillow, python, tkinter-gui
• Language: Jupyter Notebook
• Homepage: https://github.com/2000pawan/Smart-Face-Recognition-System.git
• Size: 11.3 MB
• Stars: 2
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
### 👁️ Smart Face Recognition System using Machine Learning

   - This is a GUI-based Smart Face Recognition System built using Python, Tkinter, OpenCV, and Machine Learning techniques.

   - It can recognize faces in real-time webcam feeds capturing new training data via webcam.

--- 

## 🔍 Features

 - 📹 Real-time face recognition using webcam

 - 🧠 Train and predict using PCA + Logistic Regression model

 - 🖼️ Capture 1000 images for a new user and store them in dataset

 - 🔐 Integrated simple login system

 - 🖥️ Interactive GUI made with Tkinter

--- 

## 🛠️ Technologies Used

 

 - Python

 - Tkinter (GUI)

 - OpenCV (Computer Vision)

 - PIL (Image Handling)

 - Scikit-learn (Machine Learning)

 - Haar Cascade (Face Detection)

--- 

## 📁 Project Structure

        Smart-Face-Recognition-System/

            ├── images/                              # Captured images folder collect images for training (per user)

            ├── haarcascade_frontalface_default.xml # Haar Cascade model for face detection

            ├── app.py                               # Main application code

            ├── screenshots/                         # UI screenshots

               │   ├── login_page.png

               │   ├── main_menu.png

               │   ├── image_recognition.png

            ├── README.md                            # Project documentation

---

## 🚀 Getting Started

### 1. Clone the Repository

         https://github.com/2000pawan/Smart-Face-Recognition-System.git

         cd Smart-Face-Recognition-System

### 2. Install Dependencies

Ensure you have Python 3 installed. Then, install the required libraries:

          pip install opencv-python Pillow numpy scikit-learn joblib

### 3. Run the Application

            python app.py

### 4. Login Credentials

  

  - Username: admin

  - Password: admin

## 🧠 How the Model Works

  - The system uses:

     - PCA (Principal Component Analysis) for dimensionality reduction

     - Logistic Regression for face classification

   - Steps:

      - Captured images are converted to grayscale and resized.

      - PCA reduces high-dimensional face vectors.

      - Logistic Regression predicts the identity.

## 🖼️ Screenshots

### 🔐 Login Page

![Login Page](login.png)

### 🏠 Main Menu

![Main Menu](main.png)

### 📦 Sample Output

![Prediction](image_prediction1.png)

![Prediction](image_prediction2.png)

![Prediction](image_prediction3.png)

## 📌 Notes

Make sure haarcascade_frontalface_default.xml is in the root folder.

Capture training images using the "Capture Screen" before training a new model.

## 👨‍💻 Developed By

PAWAN YADAV

AI Engineer | 2025

📧 Contact: yaduvanshi2000pawan@gmail.com

## 📜 License

This project is licensed under the MIT License – feel free to use and modify for personal or academic purposes.