🛡️ Smart Attendance System with Classroom Management

A professional, AI-powered attendance solution utilizing Face Recognition (Dlib) and Liveness Detection (Blink Detection). This system is designed for teachers and administrators to manage multiple courses, students, and attendance logs through a single, intuitive dashboard.

🚀 Key Features

• Classroom Management: Organize students by Course/Class (e.g., "Math 101", "Computer Vision").
• Native UI Registration: Register students with 20-frame face capture directly in the web dashboard—no terminal commands required!
• Anti-Spoofing (Liveness): Prevents photo or video fraud by requiring a physical Blink (Eye Aspect Ratio method) to confirm attendance.
• Flexible Tracking: Supports multiple entries per day with a 5-minute cooldown to prevent duplicate flooding.
• Administrative Control:
  • Manage Students: Edit profile details, change courses, or permanently remove students and their datasets.
  • Log Deletion: Delete individual attendance entries if recorded by mistake without affecting student records.
  • Advanced Filtering: Filter history by Course, Name, or Date and export reports to CSV.

🛠️ Tech Stack

• Languages: Python 3.x
• Computer Vision: OpenCV, Dlib, face_recognition
• User Interface: Streamlit
• Data Layer: SQLite3, Pandas
• Aesthetics: Premium Dark/Modern UI with interactive feedback and animations.

📦 Installation & Setup

1. System Dependencies (macOS)

Dlib requires C++ build tools. Install them via Homebrew:

brew install cmake boost libpng jpeg-turbo

2. Environment Setup

Clone the repository and install the Python dependencies:

git clone https://github.com/[your-username]/smart-attendance-system.git
cd smart-attendance-system
pip install -r requirements.txt

3. Required Models

Download the facial landmark predictor and place it in the project root:

• shape_predictor_68_face_landmarks.dat

🏃 Usage Guide

1. Launch the Dashboard:

   streamlit run app.py

2. Phase 1: Registration: Go to "Register User", enter the Student Name, ID, and Course, then start the live capture.
3. Phase 2: Attendance: Students stand in front of the "Attendance Tracker" and blink. The success message confirms their name and class.
4. Phase 3: Management: Use "Attendance History" to filter by course or delete specific incorrect logs.

📂 Project Structure

.
├── app.py                # Main Dashboard (Tracker, History, Registration, Management)
├── src/
│   ├── liveness.py       # EAR Blink Detection logic
│   ├── recognize.py      # Face Identification & Encoding logic
│   └── attendance.py     # Database Schema & Classroom logic
├── dataset/              # Student Image Datasets (Local only)
├── encodings/            # AI Model Encodings (Local only)
├── attendance.db         # SQLite Database (Local only)
└── requirements.txt      # Dependencies