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diff --git a/src/facial_recognition/mobilefacenetmodel/QUICKSTART.md b/src/facial_recognition/mobilefacenetmodel/QUICKSTART.md
deleted file mode 100644
index 14aeaca..0000000
--- a/src/facial_recognition/mobilefacenetmodel/QUICKSTART.md
+++ /dev/null
@@ -1,302 +0,0 @@
-# Quick Start Guide - Face Recognition on Jetson Nano
-
-## 🚀 5-Minute Setup
-
-### Step 1: Installation (5 minutes)
-
-```bash
-# Make installation script executable
-chmod +x install_jetson.sh
-
-# Run installation
-./install_jetson.sh
-
-# Wait for installation to complete
-```
-
-### Step 2: Prepare Training Data (5 minutes)
-
-Create this folder structure:
-
-```
-training_data/
-├── alice/          # 40 images of Alice
-│   ├── img001.jpg
-│   ├── img002.jpg
-│   └── ...
-├── bob/            # 40 images of Bob
-│   ├── img001.jpg
-│   └── ...
-├── charlie/        # 40 images of Charlie
-│   └── ...
-└── diana/          # 40 images of Diana
-    └── ...
-```
-
-### Step 3: Build Database (2 minutes)
-
-```bash
-python3 face_recognition_pipeline.py
-```
-
-When prompted:
-- Type `y` and press Enter
-- Type `training_data` (or your folder path)
-- Wait for processing (~2 minutes for 160 images)
-
-### Step 4: Run Live Recognition
-
-The system starts automatically after building the database!
-
-Press `q` to quit.
-
----
-
-## 📊 What to Expect
-
-### Performance Metrics
-- **FPS**: 15-25 frames per second
-- **Accuracy**: >95% for well-trained faces
-- **Latency**: <100ms per frame
-
-### First Run
-- Model download: ~30 seconds (one-time)
-- Database building: ~1-2 minutes
-- Recognition starts immediately after
-
----
-
-## 🎯 Quick Commands
-
-### Build Database Only
-```bash
-python3 database_manager.py
-# Choose option 3: Batch add from training data folder
-```
-
-### Test with Single Image
-```bash
-python3 test_pipeline.py
-# Choose option 1: Test with a single image
-```
-
-### View Database
-```bash
-python3 database_manager.py
-# Choose option 1: List all people
-```
-
-### Maximum Performance Mode
-```bash
-# Enable max performance (run before starting recognition)
-sudo nvpmodel -m 0
-sudo jetson_clocks
-```
-
----
-
-## 🔧 Common Issues & Quick Fixes
-
-### Issue: Low FPS (< 10)
-
-**Solution:**
-```bash
-# Enable max performance
-sudo nvpmodel -m 0
-sudo jetson_clocks
-
-# Then edit face_recognition_pipeline.py:
-# Change line ~198: frame_skip = 3  # was 2
-```
-
-### Issue: Camera not found
-
-**Solution:**
-```bash
-# List available cameras
-ls /dev/video*
-
-# Try different camera IDs in the script
-# Edit main() function, change: camera_id=0 to camera_id=1
-```
-
-### Issue: Poor recognition accuracy
-
-**Solutions:**
-1. Add more varied training images (different angles, lighting)
-2. Lower threshold in `face_recognition_pipeline.py`:
-   ```python
-   self.threshold = 0.5  # was 0.6
-   ```
-3. Increase detection size:
-   ```python
-   det_size=(640, 640)  # was (320, 320)
-   ```
-
-### Issue: Out of memory
-
-**Solution:**
-```bash
-# Close other applications
-# Reduce detection size in face_recognition_pipeline.py:
-det_size=(240, 240)  # was (320, 320)
-```
-
----
-
-## 📁 File Guide
-
-| File | Purpose | When to Use |
-|------|---------|-------------|
-| `face_recognition_pipeline.py` | Main system | Run this to start recognition |
-| `database_manager.py` | Manage database | Add/remove people |
-| `test_pipeline.py` | Test system | Verify before live run |
-| `install_jetson.sh` | Installation | Run once on setup |
-
----
-
-## 🎓 Tips for Best Results
-
-### Training Images
-✅ **DO:**
-- Use clear, well-lit photos
-- Include variety (happy, neutral, serious)
-- Capture different angles (±15 degrees)
-- Use consistent quality images
-
-❌ **DON'T:**
-- Use blurry or dark images
-- Include multiple people in frame
-- Use extreme angles or occlusions
-- Mix high and low quality images
-
-### Live Recognition
-✅ **DO:**
-- Ensure good lighting
-- Face camera directly
-- Stay within 1-3 meters
-- Keep face clearly visible
-
-❌ **DON'T:**
-- Use in very dark environments
-- Wear masks or heavy glasses
-- Stand too far or too close
-- Move too quickly
-
----
-
-## 🔄 Updating the Database
-
-### Add New Person
-```bash
-python3 database_manager.py
-# Choose: 2. Add person from folder
-# Enter name and folder path
-```
-
-### Remove Person
-```bash
-python3 database_manager.py
-# Choose: 4. Remove person
-# Enter name to remove
-```
-
-### Rebuild Everything
-```bash
-python3 database_manager.py
-# Choose: 6. Clear database
-# Then run main script again to rebuild
-```
-
----
-
-## 📈 Performance Tuning
-
-### Preset Configurations
-
-**Balanced (Default)**
-```python
-det_size=(320, 320)
-frame_skip = 2
-# Expected: 15-20 FPS, High accuracy
-```
-
-**Speed Priority**
-```python
-det_size=(240, 240)
-frame_skip = 3
-# Expected: 25-30 FPS, Good accuracy
-```
-
-**Quality Priority**
-```python
-det_size=(640, 640)
-frame_skip = 1
-# Expected: 8-12 FPS, Very high accuracy
-```
-
-Edit these values in `face_recognition_pipeline.py` around lines 25-27 and 198.
-
----
-
-## 🆘 Getting Help
-
-### Check System Status
-```bash
-# Check GPU usage
-sudo tegrastats
-
-# Check CUDA
-nvcc --version
-
-# Check Python packages
-pip3 list | grep -E "insightface|opencv|onnx"
-```
-
-### Useful Debug Commands
-```bash
-# Test camera
-gst-launch-1.0 nvarguscamerasrc ! nvoverlaysink
-
-# Check model files
-ls -lh ~/.insightface/models/
-
-# View database
-python3 -c "from face_recognition_pipeline import *; p = FaceRecognitionPipeline(); print(f'People: {list(p.face_database.keys())}')"
-```
-
----
-
-## ✅ Verification Checklist
-
-Before running live recognition:
-
-- [ ] Installation completed successfully
-- [ ] Training images organized in folders
-- [ ] Database built (check for `face_database/embeddings.pkl`)
-- [ ] Camera connected and detected
-- [ ] Max performance mode enabled (optional but recommended)
-- [ ] Tested with single image (optional)
-
----
-
-## 🎉 You're Ready!
-
-Run:
-```bash
-python3 face_recognition_pipeline.py
-```
-
-And start recognizing faces in real-time!
-
----
-
-## 📞 Support Resources
-
-- **Jetson Nano Forums**: https://forums.developer.nvidia.com/
-- **InsightFace Docs**: https://github.com/deepinsight/insightface
-- **OpenCV Tutorials**: https://docs.opencv.org/
-
----
-
-**Happy Face Recognizing! 🎭**
diff --git a/src/facial_recognition/mobilefacenetmodel/README.md b/src/facial_recognition/mobilefacenetmodel/README.md
deleted file mode 100644
index e8874af..0000000
--- a/src/facial_recognition/mobilefacenetmodel/README.md
+++ /dev/null
@@ -1,296 +0,0 @@
-# Live Facial Recognition for NVIDIA Jetson Nano
-
-A complete pipeline for real-time facial recognition using MobileFaceNet, optimized for NVIDIA Jetson Nano.
-
-## Features
-
-- ✅ Real-time face detection and recognition
-- ✅ Lightweight MobileFaceNet model (optimized for embedded devices)
-- ✅ GPU acceleration via CUDA
-- ✅ 15-25 FPS on Jetson Nano
-- ✅ Simple database management
-- ✅ Multi-face detection and recognition
-
-## Hardware Requirements
-
-- NVIDIA Jetson Nano (4GB recommended)
-- USB Camera or CSI Camera
-- microSD card (32GB+ recommended)
-- Power supply (5V 4A recommended for stable performance)
-
-## Software Requirements
-
-- JetPack 4.6 or later
-- Python 3.6+
-- CUDA 10.2+
-
-## Installation
-
-### Step 1: Clone or Copy Files
-
-Copy these files to your Jetson Nano:
-- `face_recognition_pipeline.py`
-- `install_jetson.sh`
-- `requirements.txt`
-
-### Step 2: Run Installation Script
-
-```bash
-chmod +x install_jetson.sh
-./install_jetson.sh
-```
-
-This will install all required dependencies including:
-- OpenCV
-- InsightFace
-- ONNX Runtime (GPU)
-- NumPy, SciPy, etc.
-
-**Note:** First run will download the face detection model (~5MB) automatically.
-
-### Alternative: Manual Installation
-
-```bash
-# Update system
-sudo apt-get update && sudo apt-get upgrade -y
-
-# Install dependencies
-sudo apt-get install -y python3-pip python3-opencv libopencv-dev
-
-# Install Python packages
-pip3 install -r requirements.txt
-```
-
-## Preparing Your Training Data
-
-### Step 1: Organize Images
-
-Create a folder structure like this:
-
-```
-training_data/
-├── person1/
-│   ├── img001.jpg
-│   ├── img002.jpg
-│   ├── ...
-│   └── img040.jpg
-├── person2/
-│   ├── img001.jpg
-│   └── ...
-├── person3/
-│   └── ...
-└── person4/
-    └── ...
-```
-
-### Step 2: Image Guidelines
-
-For best results:
-- Use **40 images per person** (as you mentioned)
-- Images should contain **clear, frontal faces**
-- Vary lighting conditions slightly
-- Include different expressions
-- Resolution: 640x480 or higher
-- Format: JPG, PNG, or BMP
-
-## Usage
-
-### Basic Usage
-
-```bash
-python3 face_recognition_pipeline.py
-```
-
-When prompted:
-1. Enter 'y' to build the database
-2. Enter the path to your training_data folder
-3. Wait for processing (1-2 minutes for 160 images)
-4. Live recognition will start automatically
-
-### Advanced Usage
-
-You can also use the pipeline programmatically:
-
-```python
-from face_recognition_pipeline import FaceRecognitionPipeline
-
-# Initialize
-pipeline = FaceRecognitionPipeline()
-
-# Add people to database
-pipeline.add_person_to_database("John", "training_data/person1")
-pipeline.add_person_to_database("Jane", "training_data/person2")
-pipeline.save_database()
-
-# Run live recognition
-pipeline.run_live_recognition(camera_id=0, display=True)
-```
-
-## Configuration Options
-
-### Adjust Recognition Threshold
-
-In `face_recognition_pipeline.py`, modify:
-
-```python
-self.threshold = 0.6  # Lower = stricter (0.5-0.7 recommended)
-```
-
-### Change Detection Size
-
-For faster processing (lower accuracy):
-```python
-self.app.prepare(ctx_id=0, det_size=(240, 240))  # Default: (320, 320)
-```
-
-For better accuracy (slower):
-```python
-self.app.prepare(ctx_id=0, det_size=(640, 640))
-```
-
-### Frame Skip
-
-Process every Nth frame for better FPS:
-```python
-frame_skip = 2  # Process every 2nd frame (default)
-frame_skip = 3  # Process every 3rd frame (faster)
-frame_skip = 1  # Process every frame (slower but smoother)
-```
-
-## Performance Optimization Tips
-
-### 1. TensorRT Conversion (Advanced)
-
-For maximum performance, convert the model to TensorRT:
-
-```python
-# This requires additional setup
-# See: https://github.com/NVIDIA-AI-IOT/torch2trt
-```
-
-### 2. Reduce Camera Resolution
-
-```python
-cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)   # Lower for more FPS
-cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
-```
-
-### 3. Use Power Mode 0 (Max Performance)
-
-```bash
-sudo nvpmodel -m 0
-sudo jetson_clocks
-```
-
-### 4. Disable GUI (Headless Mode)
-
-```python
-pipeline.run_live_recognition(camera_id=0, display=False)
-```
-
-## Troubleshooting
-
-### Low FPS (<10 FPS)
-
-- Enable MAX power mode: `sudo nvpmodel -m 0`
-- Increase frame_skip to 3 or 4
-- Reduce detection size to (240, 240)
-- Lower camera resolution
-
-### "CUDA out of memory" Error
-
-- Reduce det_size
-- Lower camera resolution
-- Close other applications
-
-### Camera Not Detected
-
-```bash
-# List cameras
-ls /dev/video*
-
-# Test camera
-gst-launch-1.0 nvarguscamerasrc ! nvoverlaysink
-
-# For USB camera, try different IDs
-pipeline.run_live_recognition(camera_id=1)
-```
-
-### Model Download Fails
-
-Download manually:
-```bash
-mkdir -p ~/.insightface/models/buffalo_sc
-# Download from: https://github.com/deepinsight/insightface/releases
-```
-
-## Expected Performance
-
-On NVIDIA Jetson Nano:
-
-| Configuration | FPS | Accuracy |
-|--------------|-----|----------|
-| Default (320x320 det) | 15-20 | High |
-| Fast (240x240 det, skip=3) | 25-30 | Good |
-| Quality (640x640 det) | 8-12 | Very High |
-
-## File Structure
-
-```
-.
-├── face_recognition_pipeline.py  # Main pipeline script
-├── install_jetson.sh             # Installation script
-├── requirements.txt              # Python dependencies
-├── README.md                     # This file
-├── training_data/                # Your training images
-│   ├── person1/
-│   ├── person2/
-│   ├── person3/
-│   └── person4/
-└── face_database/                # Generated database
-    └── embeddings.pkl            # Stored face embeddings
-```
-
-## How It Works
-
-1. **Face Detection**: Uses a lightweight RetinaFace detector to find faces in frames
-2. **Feature Extraction**: MobileFaceNet extracts 512-dimensional embeddings
-3. **Database Building**: Averages embeddings from 40 images per person
-4. **Recognition**: Compares live embeddings to database using cosine similarity
-5. **Real-time Display**: Shows bounding boxes and names with confidence scores
-
-## Model Details
-
-- **Face Detection**: RetinaFace (mobile version)
-- **Face Recognition**: MobileFaceNet
-- **Embedding Size**: 512 dimensions
-- **Model Size**: ~5MB total
-- **Inference Backend**: ONNX Runtime (GPU accelerated)
-
-## API Reference
-
-### FaceRecognitionPipeline
-
-#### Methods
-
-- `__init__(database_path, model_name)` - Initialize pipeline
-- `add_person_to_database(person_name, image_folder)` - Add person to database
-- `save_database()` - Save database to disk
-- `load_database()` - Load database from disk
-- `recognize_face(embedding)` - Recognize a face from embedding
-- `run_live_recognition(camera_id, display)` - Run live recognition
-
-## License
-
-This project uses InsightFace which is licensed under MIT License.
-
-## Credits
-
-- InsightFace: https://github.com/deepinsight/insightface
-- MobileFaceNet: https://arxiv.org/abs/1804.07573
-
-## Support
-
-For issues specific to:
-- Jetson Nano setup: https://forums.developer.nvidia.com/
-- InsightFace: https://github.com/deepinsight/insightface/issues
diff --git a/src/facial_recognition/mobilefacenetmodel/database_manager.py b/src/facial_recognition/mobilefacenetmodel/database_manager.py
deleted file mode 100644
index e46fa23..0000000
--- a/src/facial_recognition/mobilefacenetmodel/database_manager.py
+++ /dev/null
@@ -1,223 +0,0 @@
-"""
-Database Management Utility
-Tools for managing the face recognition database
-"""
-
-from face_recognition_pipeline import FaceRecognitionPipeline
-from pathlib import Path
-import json
-import numpy as np
-
-class DatabaseManager:
-    def __init__(self, pipeline=None):
-        if pipeline is None:
-            self.pipeline = FaceRecognitionPipeline()
-        else:
-            self.pipeline = pipeline
-    
-    def list_people(self):
-        """List all people in the database"""
-        print("\n" + "="*50)
-        print("PEOPLE IN DATABASE")
-        print("="*50)
-        
-        if len(self.pipeline.face_database) == 0:
-            print("Database is empty")
-            return
-        
-        for idx, name in enumerate(self.pipeline.face_database.keys(), 1):
-            embedding = self.pipeline.face_database[name]
-            print(f"{idx}. {name} (embedding dim: {len(embedding)})")
-    
-    def remove_person(self, person_name):
-        """Remove a person from the database"""
-        if person_name in self.pipeline.face_database:
-            del self.pipeline.face_database[person_name]
-            self.pipeline.save_database()
-            print(f"✓ Removed {person_name} from database")
-            return True
-        else:
-            print(f"✗ {person_name} not found in database")
-            return False
-    
-    def clear_database(self):
-        """Clear entire database"""
-        confirm = input("Are you sure you want to clear the entire database? (yes/no): ")
-        if confirm.lower() == 'yes':
-            self.pipeline.face_database = {}
-            self.pipeline.save_database()
-            print("✓ Database cleared")
-        else:
-            print("Operation cancelled")
-    
-    def export_database_info(self, output_file="database_info.json"):
-        """Export database information to JSON"""
-        info = {
-            "num_people": len(self.pipeline.face_database),
-            "people": list(self.pipeline.face_database.keys()),
-            "threshold": self.pipeline.threshold,
-            "embedding_dimension": len(next(iter(self.pipeline.face_database.values()))) if self.pipeline.face_database else 0
-        }
-        
-        with open(output_file, 'w') as f:
-            json.dump(info, f, indent=2)
-        
-        print(f"✓ Database info exported to {output_file}")
-    
-    def batch_add_from_folder(self, training_data_folder):
-        """
-        Add all people from a training data folder
-        
-        Expected structure:
-        training_data/
-            ├── person1/
-            ├── person2/
-            └── ...
-        """
-        training_data_path = Path(training_data_folder)
-        
-        if not training_data_path.exists():
-            print(f"Error: {training_data_folder} does not exist")
-            return
-        
-        success_count = 0
-        fail_count = 0
-        
-        for person_folder in training_data_path.iterdir():
-            if person_folder.is_dir():
-                person_name = person_folder.name
-                print(f"\nProcessing {person_name}...")
-                
-                if self.pipeline.add_person_to_database(person_name, person_folder):
-                    success_count += 1
-                else:
-                    fail_count += 1
-        
-        if success_count > 0:
-            self.pipeline.save_database()
-        
-        print(f"\n{'='*50}")
-        print(f"Batch processing complete:")
-        print(f"  Success: {success_count}")
-        print(f"  Failed: {fail_count}")
-        print(f"{'='*50}")
-    
-    def verify_database(self):
-        """Verify database integrity"""
-        print("\nVerifying database...")
-        
-        if len(self.pipeline.face_database) == 0:
-            print("✗ Database is empty")
-            return False
-        
-        issues = []
-        
-        for name, embedding in self.pipeline.face_database.items():
-            # Check embedding dimension
-            if len(embedding) != 512:
-                issues.append(f"{name}: incorrect embedding dimension ({len(embedding)})")
-            
-            # Check for NaN or Inf values
-            if np.any(np.isnan(embedding)) or np.any(np.isinf(embedding)):
-                issues.append(f"{name}: contains NaN or Inf values")
-        
-        if issues:
-            print("✗ Issues found:")
-            for issue in issues:
-                print(f"  - {issue}")
-            return False
-        else:
-            print(f"✓ Database verified successfully ({len(self.pipeline.face_database)} people)")
-            return True
-    
-    def update_person(self, person_name, new_image_folder):
-        """Update embeddings for an existing person"""
-        print(f"Updating {person_name}...")
-        
-        # Remove old entry
-        if person_name in self.pipeline.face_database:
-            del self.pipeline.face_database[person_name]
-        
-        # Add new entry
-        if self.pipeline.add_person_to_database(person_name, new_image_folder):
-            self.pipeline.save_database()
-            print(f"✓ Updated {person_name}")
-            return True
-        else:
-            print(f"✗ Failed to update {person_name}")
-            return False
-
-
-def main():
-    """Interactive database management"""
-    print("="*50)
-    print("DATABASE MANAGEMENT UTILITY")
-    print("="*50)
-    
-    manager = DatabaseManager()
-    
-    while True:
-        print("\nOptions:")
-        print("1. List all people")
-        print("2. Add person from folder")
-        print("3. Batch add from training data folder")
-        print("4. Remove person")
-        print("5. Update person")
-        print("6. Clear database")
-        print("7. Verify database")
-        print("8. Export database info")
-        print("9. Exit")
-        
-        choice = input("\nEnter your choice (1-9): ")
-        
-        if choice == '1':
-            manager.list_people()
-        
-        elif choice == '2':
-            name = input("Enter person name: ")
-            folder = input("Enter folder path: ")
-            if Path(folder).exists():
-                manager.pipeline.add_person_to_database(name, folder)
-                manager.pipeline.save_database()
-            else:
-                print(f"Error: Folder {folder} not found")
-        
-        elif choice == '3':
-            folder = input("Enter training data folder path: ")
-            manager.batch_add_from_folder(folder)
-        
-        elif choice == '4':
-            manager.list_people()
-            name = input("\nEnter name to remove: ")
-            manager.remove_person(name)
-        
-        elif choice == '5':
-            manager.list_people()
-            name = input("\nEnter name to update: ")
-            folder = input("Enter new image folder path: ")
-            if Path(folder).exists():
-                manager.update_person(name, folder)
-            else:
-                print(f"Error: Folder {folder} not found")
-        
-        elif choice == '6':
-            manager.clear_database()
-        
-        elif choice == '7':
-            manager.verify_database()
-        
-        elif choice == '8':
-            filename = input("Enter output filename (default: database_info.json): ")
-            if not filename:
-                filename = "database_info.json"
-            manager.export_database_info(filename)
-        
-        elif choice == '9':
-            print("Exiting...")
-            break
-        
-        else:
-            print("Invalid choice")
-
-if __name__ == "__main__":
-    main()
diff --git a/src/facial_recognition/mobilefacenetmodel/face_database/embeddings.pkl b/src/facial_recognition/mobilefacenetmodel/face_database/embeddings.pkl
deleted file mode 100644
index 7ba39c9..0000000
Binary files a/src/facial_recognition/mobilefacenetmodel/face_database/embeddings.pkl and /dev/null differ
diff --git a/src/facial_recognition/mobilefacenetmodel/face_recognition_pipeline.py b/src/facial_recognition/mobilefacenetmodel/face_recognition_pipeline.py
deleted file mode 100644
index 68896e2..0000000
--- a/src/facial_recognition/mobilefacenetmodel/face_recognition_pipeline.py
+++ /dev/null
@@ -1,274 +0,0 @@
-"""
-Live Facial Recognition Pipeline for NVIDIA Jetson Nano
-Using MobileFaceNet and InsightFace
-"""
-
-import cv2
-import numpy as np
-from insightface.app import FaceAnalysis
-from scipy.spatial.distance import cosine
-import pickle
-import os
-from pathlib import Path
-import time
-
-class FaceRecognitionPipeline:
-    def __init__(self, database_path='face_database', model_name='buffalo_sc'):
-        """
-        Initialize the face recognition pipeline
-        
-        Args:
-            database_path: Path to store face embeddings database
-            model_name: InsightFace model name ('buffalo_sc' is lightweight)
-        """
-        self.database_path = Path(database_path)
-        self.database_path.mkdir(exist_ok=True)
-        
-        # Initialize InsightFace with lightweight model
-        print("Loading face detection and recognition models...")
-        self.app = FaceAnalysis(
-            name=model_name,
-            providers=['CUDAExecutionProvider', 'CPUExecutionProvider']
-        )
-        # det_size=(320, 320) for faster detection on Jetson Nano
-        self.app.prepare(ctx_id=0, det_size=(320, 320))
-        
-        # Load or initialize face database
-        self.face_database = {}
-        self.database_file = self.database_path / 'embeddings.pkl'
-        self.load_database()
-        
-        # Recognition threshold (lower = stricter)
-        self.threshold = 0.6
-        
-    def extract_embedding(self, image):
-        """
-        Extract face embedding from image
-        
-        Args:
-            image: BGR image from OpenCV
-            
-        Returns:
-            embedding vector or None if no face detected
-        """
-        faces = self.app.get(image)
-        
-        if len(faces) == 0:
-            return None
-        
-        # Return embedding of the largest face
-        largest_face = max(faces, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]))
-        return largest_face.embedding
-    
-    def add_person_to_database(self, person_name, image_folder):
-        """
-        Add a person to the database using multiple images
-        
-        Args:
-            person_name: Name/ID of the person
-            image_folder: Path to folder containing person's images
-        """
-        print(f"Processing images for {person_name}...")
-        image_folder = Path(image_folder)
-        embeddings = []
-        
-        # Supported image formats
-        image_extensions = ['.jpg', '.jpeg', '.png', '.bmp']
-        
-        for img_path in image_folder.iterdir():
-            if img_path.suffix.lower() in image_extensions:
-                image = cv2.imread(str(img_path))
-                if image is None:
-                    print(f"Warning: Could not read {img_path}")
-                    continue
-                
-                embedding = self.extract_embedding(image)
-                if embedding is not None:
-                    embeddings.append(embedding)
-                else:
-                    print(f"Warning: No face detected in {img_path}")
-        
-        if len(embeddings) == 0:
-            print(f"Error: No valid embeddings extracted for {person_name}")
-            return False
-        
-        # Store average embedding for the person
-        avg_embedding = np.mean(embeddings, axis=0)
-        self.face_database[person_name] = avg_embedding
-        
-        print(f"Added {person_name} with {len(embeddings)} embeddings")
-        return True
-    
-    def save_database(self):
-        """Save face database to disk"""
-        with open(self.database_file, 'wb') as f:
-            pickle.dump(self.face_database, f)
-        print(f"Database saved to {self.database_file}")
-    
-    def load_database(self):
-        """Load face database from disk"""
-        if self.database_file.exists():
-            with open(self.database_file, 'rb') as f:
-                self.face_database = pickle.load(f)
-            print(f"Loaded database with {len(self.face_database)} people")
-        else:
-            print("No existing database found. Starting fresh.")
-    
-    def recognize_face(self, embedding):
-        """
-        Recognize a face by comparing embedding to database
-        
-        Args:
-            embedding: Face embedding vector
-            
-        Returns:
-            (person_name, confidence) or (None, 0) if no match
-        """
-        if len(self.face_database) == 0:
-            return None, 0
-        
-        best_match = None
-        best_distance = float('inf')
-        
-        for person_name, stored_embedding in self.face_database.items():
-            # Calculate cosine distance
-            distance = cosine(embedding, stored_embedding)
-            
-            if distance < best_distance:
-                best_distance = distance
-                best_match = person_name
-        
-        # Convert distance to similarity score
-        similarity = 1 - best_distance
-        
-        if similarity >= self.threshold:
-            return best_match, similarity
-        else:
-            return "Unknown", similarity
-    
-    def run_live_recognition(self, camera_id=0, display=True):
-        """
-        Run live face recognition from camera
-        
-        Args:
-            camera_id: Camera device ID (0 for default)
-            display: Whether to display video feed
-        """
-        cap = cv2.VideoCapture(camera_id)
-        
-        # Set camera resolution (lower resolution = faster processing)
-        cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
-        cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
-        
-        print("Starting live recognition. Press 'q' to quit.")
-        
-        # For FPS calculation
-        fps_counter = 0
-        fps_start_time = time.time()
-        current_fps = 0
-        
-        # Process every N frames to improve speed
-        frame_skip = 2
-        frame_count = 0
-        
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                print("Failed to grab frame")
-                break
-            
-            frame_count += 1
-            
-            # Skip frames for better performance
-            if frame_count % frame_skip != 0:
-                if display:
-                    cv2.imshow('Face Recognition', frame)
-                if cv2.waitKey(1) & 0xFF == ord('q'):
-                    break
-                continue
-            
-            # Detect and recognize faces
-            faces = self.app.get(frame)
-            
-            for face in faces:
-                # Get bounding box
-                bbox = face.bbox.astype(int)
-                x1, y1, x2, y2 = bbox
-                
-                # Recognize face
-                person_name, confidence = self.recognize_face(face.embedding)
-                
-                # Draw bounding box
-                color = (0, 255, 0) if person_name != "Unknown" else (0, 0, 255)
-                cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
-                
-                # Draw label
-                label = f"{person_name}: {confidence:.2f}"
-                label_size, _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)
-                cv2.rectangle(frame, (x1, y1 - label_size[1] - 10), 
-                            (x1 + label_size[0], y1), color, -1)
-                cv2.putText(frame, label, (x1, y1 - 5), 
-                          cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
-            
-            # Calculate FPS
-            fps_counter += 1
-            if fps_counter >= 10:
-                current_fps = fps_counter / (time.time() - fps_start_time)
-                fps_counter = 0
-                fps_start_time = time.time()
-            
-            # Display FPS
-            cv2.putText(frame, f"FPS: {current_fps:.1f}", (10, 30),
-                       cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-            
-            if display:
-                cv2.imshow('Face Recognition', frame)
-            
-            # Check for quit
-            if cv2.waitKey(1) & 0xFF == ord('q'):
-                break
-        
-        cap.release()
-        cv2.destroyAllWindows()
-
-
-def main():
-    """Example usage of the pipeline"""
-    
-    # Initialize pipeline
-    pipeline = FaceRecognitionPipeline()
-    
-    # Example: Build database from folders
-    # Assuming you have a structure like:
-    # training_data/
-    #   ├── person1/
-    #   │   ├── img1.jpg
-    #   │   ├── img2.jpg
-    #   │   └── ...
-    #   ├── person2/
-    #   │   └── ...
-    
-    build_database = input("Do you want to build/rebuild the database? (y/n): ")
-    
-    if build_database.lower() == 'y':
-        training_data_path = input("Enter path to training data folder: ")
-        training_data_path = Path(training_data_path)
-        
-        if training_data_path.exists():
-            for person_folder in training_data_path.iterdir():
-                if person_folder.is_dir():
-                    person_name = person_folder.name
-                    pipeline.add_person_to_database(person_name, person_folder)
-            
-            pipeline.save_database()
-        else:
-            print(f"Error: Path {training_data_path} does not exist")
-            return
-    
-    # Run live recognition
-    print("\nStarting live face recognition...")
-    pipeline.run_live_recognition(camera_id=0, display=True)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/facial_recognition/mobilefacenetmodel/install_jetson.sh b/src/facial_recognition/mobilefacenetmodel/install_jetson.sh
deleted file mode 100644
index 10499d5..0000000
--- a/src/facial_recognition/mobilefacenetmodel/install_jetson.sh
+++ /dev/null
@@ -1,82 +0,0 @@
-#!/bin/bash
-
-# Installation script for Face Recognition on NVIDIA Jetson Nano
-# This script installs all required dependencies
-
-echo "==================================================="
-echo "Face Recognition Setup for NVIDIA Jetson Nano"
-echo "==================================================="
-
-# Update system
-echo "Updating system packages..."
-sudo apt-get update
-sudo apt-get upgrade -y
-
-# Install Python dependencies
-echo "Installing Python and pip..."
-sudo apt-get install -y python3-pip python3-dev
-
-# Install OpenCV dependencies
-echo "Installing OpenCV dependencies..."
-sudo apt-get install -y \
-    libopencv-dev \
-    python3-opencv \
-    libgtk-3-dev \
-    libcanberra-gtk-module \
-    libcanberra-gtk3-module
-
-# Install other required libraries
-echo "Installing system libraries..."
-sudo apt-get install -y \
-    libhdf5-dev \
-    libhdf5-serial-dev \
-    libatlas-base-dev \
-    libjasper-dev \
-    libqtgui4 \
-    libqt4-test
-
-# Upgrade pip
-echo "Upgrading pip..."
-pip3 install --upgrade pip
-
-# Install Python packages
-echo "Installing Python packages..."
-pip3 install --upgrade setuptools wheel
-
-# Install numpy (critical for Jetson)
-echo "Installing numpy..."
-pip3 install numpy
-
-# Install scipy
-echo "Installing scipy..."
-pip3 install scipy
-
-# Install onnxruntime-gpu for Jetson
-echo "Installing onnxruntime for GPU acceleration..."
-# For Jetson Nano with JetPack 4.6+
-pip3 install onnxruntime-gpu
-
-# Install InsightFace
-echo "Installing InsightFace..."
-pip3 install insightface
-
-# Install additional dependencies
-pip3 install scikit-learn
-
-echo ""
-echo "==================================================="
-echo "Installation complete!"
-echo "==================================================="
-echo ""
-echo "Next steps:"
-echo "1. Organize your training images in folders:"
-echo "   training_data/"
-echo "     ├── person1/"
-echo "     ├── person2/"
-echo "     ├── person3/"
-echo "     └── person4/"
-echo ""
-echo "2. Run: python3 face_recognition_pipeline.py"
-echo ""
-echo "Note: First run will download the model (~5MB)"
-echo "==================================================="
diff --git a/src/facial_recognition/mobilefacenetmodel/requirements.txt b/src/facial_recognition/mobilefacenetmodel/requirements.txt
deleted file mode 100644
index 45c8946..0000000
--- a/src/facial_recognition/mobilefacenetmodel/requirements.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-numpy>=1.19.4
-opencv-python>=4.5.0
-insightface>=0.7.3
-onnxruntime>=1.8.0
-scipy>=1.5.4
-scikit-learn>=0.24.0
diff --git a/src/facial_recognition/mobilefacenetmodel/run_detection_only.py b/src/facial_recognition/mobilefacenetmodel/run_detection_only.py
deleted file mode 100644
index 8d61f60..0000000
--- a/src/facial_recognition/mobilefacenetmodel/run_detection_only.py
+++ /dev/null
@@ -1,56 +0,0 @@
-"""
-Face detection only — no database, no recognition.
-Uses the same InsightFace detector; draws bounding boxes only.
-Press 'q' to quit.
-"""
-
-import cv2
-import argparse
-from insightface.app import FaceAnalysis
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Run face detection only (no recognition)")
-    parser.add_argument("--camera", type=int, default=0, help="Camera device ID (default: 0)")
-    parser.add_argument("--det-size", type=int, default=320, help="Detection size (default: 320, use 240 for faster)")
-    parser.add_argument("--no-display", action="store_true", help="Run without opening a window (e.g. for benchmarking)")
-    args = parser.parse_args()
-
-    print("Loading face detection model...")
-    app = FaceAnalysis(
-        name="buffalo_sc",
-        providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
-    )
-    app.prepare(ctx_id=0, det_size=(args.det_size, args.det_size))
-    print("Detection only — no database or recognition. Press 'q' to quit.")
-
-    cap = cv2.VideoCapture(args.camera)
-    if not cap.isOpened():
-        print("Error: Could not open camera")
-        return
-
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        faces = app.get(frame)
-
-        for face in faces:
-            x1, y1, x2, y2 = face.bbox.astype(int)
-            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
-            cv2.putText(frame, "Face", (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
-
-        cv2.putText(frame, f"Faces: {len(faces)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-
-        if not args.no_display:
-            cv2.imshow("Face detection only", frame)
-            if cv2.waitKey(1) & 0xFF == ord("q"):
-                break
-
-    cap.release()
-    cv2.destroyAllWindows()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/facial_recognition/mobilefacenetmodel/test_pipeline.py b/src/facial_recognition/mobilefacenetmodel/test_pipeline.py
deleted file mode 100644
index b12890d..0000000
--- a/src/facial_recognition/mobilefacenetmodel/test_pipeline.py
+++ /dev/null
@@ -1,137 +0,0 @@
-"""
-Test script for face recognition pipeline
-Use this to test the system with single images before running live recognition
-"""
-
-import cv2
-from face_recognition_pipeline import FaceRecognitionPipeline
-from pathlib import Path
-import sys
-
-def test_single_image(pipeline, image_path):
-    """Test recognition on a single image"""
-    print(f"\nTesting image: {image_path}")
-    
-    image = cv2.imread(image_path)
-    if image is None:
-        print(f"Error: Could not read image {image_path}")
-        return
-    
-    # Get faces
-    faces = pipeline.app.get(image)
-    
-    if len(faces) == 0:
-        print("No faces detected in image")
-        return
-    
-    print(f"Detected {len(faces)} face(s)")
-    
-    # Process each face
-    for idx, face in enumerate(faces):
-        person_name, confidence = pipeline.recognize_face(face.embedding)
-        print(f"Face {idx + 1}: {person_name} (confidence: {confidence:.3f})")
-        
-        # Draw on image
-        bbox = face.bbox.astype(int)
-        x1, y1, x2, y2 = bbox
-        
-        color = (0, 255, 0) if person_name != "Unknown" else (0, 0, 255)
-        cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
-        
-        label = f"{person_name}: {confidence:.2f}"
-        cv2.putText(image, label, (x1, y1 - 10),
-                   cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
-    
-    # Display result
-    cv2.imshow('Test Result', image)
-    print("Press any key to continue...")
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()
-
-def test_database_info(pipeline):
-    """Display information about the current database"""
-    print("\n" + "="*50)
-    print("DATABASE INFORMATION")
-    print("="*50)
-    
-    if len(pipeline.face_database) == 0:
-        print("Database is empty!")
-        print("Please build the database first using the main script.")
-        return False
-    
-    print(f"Number of people in database: {len(pipeline.face_database)}")
-    print(f"Recognition threshold: {pipeline.threshold}")
-    print("\nPeople in database:")
-    for idx, name in enumerate(pipeline.face_database.keys(), 1):
-        print(f"  {idx}. {name}")
-    
-    print("="*50)
-    return True
-
-def test_embedding_extraction(pipeline, image_path):
-    """Test if embeddings can be extracted from an image"""
-    print(f"\nTesting embedding extraction from: {image_path}")
-    
-    image = cv2.imread(image_path)
-    if image is None:
-        print(f"Error: Could not read image")
-        return False
-    
-    embedding = pipeline.extract_embedding(image)
-    
-    if embedding is None:
-        print("❌ Failed to extract embedding (no face detected)")
-        return False
-    else:
-        print(f"✓ Successfully extracted embedding (dimension: {len(embedding)})")
-        return True
-
-def main():
-    print("="*50)
-    print("FACE RECOGNITION PIPELINE - TEST SCRIPT")
-    print("="*50)
-    
-    # Initialize pipeline
-    print("\nInitializing pipeline...")
-    pipeline = FaceRecognitionPipeline()
-    
-    # Test 1: Check database
-    print("\n[TEST 1] Checking database...")
-    has_database = test_database_info(pipeline)
-    
-    if not has_database:
-        print("\nPlease run 'python3 face_recognition_pipeline.py' first to build the database.")
-        return
-    
-    # Test 2: Test with sample images
-    print("\n[TEST 2] Testing with images...")
-    print("\nOptions:")
-    print("1. Test with a single image")
-    print("2. Test embedding extraction")
-    print("3. Exit")
-    
-    choice = input("\nEnter your choice (1-3): ")
-    
-    if choice == '1':
-        image_path = input("Enter path to test image: ")
-        if Path(image_path).exists():
-            test_single_image(pipeline, image_path)
-        else:
-            print(f"Error: File {image_path} not found")
-    
-    elif choice == '2':
-        image_path = input("Enter path to test image: ")
-        if Path(image_path).exists():
-            test_embedding_extraction(pipeline, image_path)
-        else:
-            print(f"Error: File {image_path} not found")
-    
-    elif choice == '3':
-        print("Exiting...")
-        return
-    
-    else:
-        print("Invalid choice")
-
-if __name__ == "__main__":
-    main()
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