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This is a GUI-based Smart Face Recognition System built using Python, Tkinter, OpenCV, and Machine Learning techniques.\n   - It can recognize faces in real-time webcam feeds capturing new training data via webcam.\n\n--- \n\n## 🔍 Features\n\n - 📹 Real-time face recognition using webcam\n - 🧠 Train and predict using PCA + Logistic Regression model\n - 🖼️ Capture 1000 images for a new user and store them in dataset\n - 🔐 Integrated simple login system\n - 🖥️ Interactive GUI made with Tkinter\n\n--- \n\n## 🛠️ Technologies Used\n \n - Python\n - Tkinter (GUI)\n - OpenCV (Computer Vision)\n - PIL (Image Handling)\n - Scikit-learn (Machine Learning)\n - Haar Cascade (Face Detection)\n\n--- \n\n## 📁 Project Structure\n        Smart-Face-Recognition-System/\n\n            ├── images/                              # Captured images folder collect images for training (per user)\n            ├── haarcascade_frontalface_default.xml # Haar Cascade model for face detection\n            ├── app.py                               # Main application code\n            ├── screenshots/                         # UI screenshots\n               │   ├── login_page.png\n               │   ├── main_menu.png\n               │   ├── image_recognition.png\n            ├── README.md                            # Project documentation\n\n---\n\n## 🚀 Getting Started\n\n### 1. Clone the Repository\n\n         https://github.com/2000pawan/Smart-Face-Recognition-System.git\n         cd Smart-Face-Recognition-System\n\n### 2. Install Dependencies\nEnsure you have Python 3 installed. Then, install the required libraries:\n\n          pip install opencv-python Pillow numpy scikit-learn joblib\n\n### 3. Run the Application\n\n            python app.py\n\n### 4. Login Credentials\n  \n  - Username: admin\n  - Password: admin\n\n## 🧠 How the Model Works\n\n  - The system uses:\n\n     - PCA (Principal Component Analysis) for dimensionality reduction\n     - Logistic Regression for face classification\n\n   - Steps:\n\n      - Captured images are converted to grayscale and resized.\n      - PCA reduces high-dimensional face vectors.\n      - Logistic Regression predicts the identity.\n\n\n## 🖼️ Screenshots\n\n### 🔐 Login Page\n![Login Page](login.png)\n\n### 🏠 Main Menu\n![Main Menu](main.png)\n\n### 📦 Sample Output\n![Prediction](image_prediction1.png)\n\n![Prediction](image_prediction2.png)\n\n![Prediction](image_prediction3.png)\n\n## 📌 Notes\nMake sure haarcascade_frontalface_default.xml is in the root folder.\n\nCapture training images using the \"Capture Screen\" before training a new model.\n\n## 👨‍💻 Developed By\n\nPAWAN YADAV\n\nAI Engineer | 2025\n\n📧 Contact: yaduvanshi2000pawan@gmail.com\n\n## 📜 License\nThis project is licensed under the MIT License – feel free to use and modify for personal or academic purposes.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F2000pawan%2Fsmart-face-recognition-system","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F2000pawan%2Fsmart-face-recognition-system","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F2000pawan%2Fsmart-face-recognition-system/lists"}