# Face Detection using OpenCV with Raspberry Pi

## Introduction

This project aims to develop a **portable** and **wireless** Face Recognition System (FRS) using Raspberry Pi with a Camera Module attachment. The Raspberry Pi is powered using a power-bank, enabling the development of a portable facial recognition system.   
The project makes use of the **OpenCV** (Open-source Computer Vision) library, an open-source library for computer vision and machine learning tasks with contributions from more than a thousand developers.  
We chose to use cascade classifier method specifically Haar cascade object detection for facial detection over TensorFlow due to less complexity, moderate performance, lower system requirements, and better integration.

## Architecture

![Architecture Diagram](media/architecture.png)

The Raspberry Pi acts as a wireless access point (WAP), connecting to it would allow developers and users to interact with their devices. Upon connecting with the network, it would be able to provide services such as SSH server, VNC server, and a Webserver. To access the system, developers can access the Raspberry Pi using the terminal using SSH, and VNC allows the system to be controlled remotely from a mobile device or a desktop computer. For the user side, they can view the live feed or access the database by logging in to a WebUI, which would lead them to an Authentication portal. This functionality provides cybersecurity by taking Security and Privacy Measures, ensuring that user data is protected and stored securely, and access is restricted to authorized personnel only.

## Flow

1. **Hardware Setup**: Connect a Raspberry Pi camera module to the Raspberry Pi board. We have a portable Face Detection System consisting of Raspberry Pi 4B with a Camera Module. It is also connected to a Powerbank for power.

2. **Software Setup**: Install Required Libraries: Install the OpenCV library on the Raspberry Pi using the terminal command "pip install opencv-python".

3. **Networking Setup**: Enable Wireless Access Point configuration (IEEE 802.11) and run the services SSH on port 22, VNC on port 5900, and WebUI at port 5000.

4. **Face Detection**: Load the Haar Cascade Classifier haarcascade_frontalface_default.xml file using the cv2.CascadeClassifier() function.

5. **Capture the Image**: Use OpenCV to capture the image from the Raspberry Pi camera module using the cv2.VideoCapture() function.

6. **Preprocess the Image**: Convert the captured image to grayscale using the cv2.cvtColor() function to improve the efficiency of the face detection algorithm.

7. **Detect Faces**: Use the detectMultiScale() function of the cascade classifier to detect faces in the grayscale image. It returns the coordinates of the bounding boxes.

8. **Display the Detected Faces**: Draw a rectangle around each detected face using the coordinates obtained and display the image using the cv2.imshow() function.

9. **View Detected Faces**: The displayed detected faces can be viewed by VNC or WebUI using a live feed. Upon reaching the WebUI, users will be confronted with an Authentication portal allowing them to log in as either “admin” or “viewer” for Database management, and live-feed is accessible to all.

10. **Release Resources**: Release the video capture and destroy all windows using the cv2.release() and cv2.destroyAllWindows() functions, respectively.

## Progression

![Initial Trial](media/initial_trial.png)

Initial trial- Using HaarCascade classifier for face detection and using LBPH(Local Binary Patterns Histograms) recognizer for training the model.

![LCD Screen](media/lcd_screen.png)

Using LCD 1602 I2C Screen to get the Local IP Address of the Raspberry Pi.

![Overall Setup](media/setup.png)
Overall Setup

## Wireless Adapter

Command for checking whether driver for **Bus 001 Device 003: ID 2357:010c TP-Link TL-WN722N v2/v3 [Realtek RTL8188EUS]** wireless adapter is installed or not

```yaml
lsmod | grep 8188eu
```