Face Recognition Attendance System using ESP32-CAM and OpenCV

This project is a face recognition-based attendance system using OpenCV, ESP32-CAM, and cloud integration. It captures student images, trains a recognizer, and marks attendance in real-time from an ESP32 video stream.

Features

• Face detection using OpenCV DNN.
• Face recognition using LBPH algorithm.
• Real-time face streaming from ESP32-CAM.
• Attendance logging with timestamps.
• Cloud API integration for attendance records.
• Blynk integration to fetch ESP32 IP address.

Folder Structure

• face_capture.py – Captures and saves student face images.
• train_model.py – Trains LBPH face recognizer with saved images.
• recognize_from_esp.py – Recognizes faces from ESP32 stream and marks attendance.

Requirements

pip install opencv-python opencv-contrib-python numpy requests

## 🔄 System Flow Diagram


```text
+--------------------+
|  face_capture.py   |
| (Capture faces &   |
|  store in DB)      |
+--------+-----------+
         |
         v
+--------------------+
|  train_model.py     |
| (Train LBPH model  |
|  with captured data)|
+--------+-----------+
         |
         v
+--------------------+
| recognize_from_esp.py |
| (Fetch ESP32 IP via   |
|  Blynk, Stream video, |
|  Detect & Recognize,  |
|  Send attendance to   |
|  cloud API)           |
+--------+-----------+
         |
         v
+----------------------------+
| Cloud API: mark_attendees |
| (Receive and store        |
|  attendance records)      |
+----------------------------+