🤖 AI Attendance System – Main Application

Intelligent AI Attendance - Face & Voice

Face Recognition & Voice Biometrics for Smart Attendance

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📌 Overview

The AI Attendance Application is a biometric attendance platform that uses face recognition and voice biometrics to automatically mark classroom attendance.

The application is built using Streamlit for the UI, and integrates machine learning pipelines for identity verification.

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🎯 Key Features

👨‍🏫 Teacher Features

• Create subjects or courses
• Generate QR code for enrollment
• Start face attendance
• Start voice attendance
• View attendance records
• Manage student enrollment

👨‍🎓 Student Features

• Join subjects via QR code
• Upload face profile
• Enroll voice profile
• Mark attendance using biometrics

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🧠 AI Pipelines

The system uses two biometric pipelines.

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📸 Face Recognition

Pipeline steps:

Camera Image
     │
     ▼
Face Detection (dlib)
     │
     ▼
Face Embedding Generation
     │
     ▼
Embedding Similarity Comparison
     │
     ▼
Student Identification
     │
     ▼
Attendance Logged

---

🎙️ Voice Recognition

Pipeline steps:

Audio Recording
     │
     ▼
Audio Feature Extraction (Librosa)
     │
     ▼
Speaker Embedding (Resemblyzer)
     │
     ▼
Embedding Comparison
     │
     ▼
Identity Verification
     │
     ▼
Attendance Logged

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🏗️ Project Structure

ai-attendance-project-app-main
│
├── src
│   ├── components
│   │   ├── dialog_*.py
│   │   ├── header.py
│   │   └── footer.py
│   │
│   ├── database
│   │   ├── config.py
│   │   └── db.py
│   │
│   ├── pipelines
│   │   ├── face_pipeline.py
│   │   └── voice_pipeline.py
│   │
│   ├── screens
│   │   ├── home_screen.py
│   │   ├── teacher_screen.py
│   │   └── student_screen.py
│   │
│   └── ui
│       └── base_layout.py
│
├── app.py
└── requirements.txt

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🖥️ Technology Stack

Category	Technology
UI	Streamlit
Machine Learning	dlib, scikit-learn
Voice Recognition	Resemblyzer
Audio Processing	Librosa
Data Processing	NumPy, Pandas
Image Processing	Pillow
Database	Supabase
Security	bcrypt

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⚙️ Installation

Navigate to the application folder:

cd ai-attendance-project-app-main

Create virtual environment:

python -m venv venv

Activate environment:

Windows:

venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

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▶️ Running the Application

Start the Streamlit app:

streamlit run app.py

Open in browser:

http://localhost:8501

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🗄️ Database

The application uses Supabase for cloud database management.

Stored data includes:

• Students
• Subjects
• Enrollment records
• Face embeddings
• Voice embeddings
• Attendance logs

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🔬 Methodology

Identity verification is performed using embedding similarity comparison.

Steps:

1. Extract biometric embedding
2. Compute similarity with stored embeddings
3. Apply similarity threshold
4. Identify user
5. Record attendance

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🚀 Future Improvements

• Face liveness detection
• Anti-spoofing mechanisms
• Mobile device support
• Attendance analytics dashboard
• Real-time attendance monitoring

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👨‍💻 Author

Satinder Singh Sall AI / ML Full-Stack Web & Mobile Engineer

Developed as part of the AI Projects Module

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📜 License

This project is licensed under the MIT License.