WIFI CLOCK SPY CAM

Abstract

This project aims to create an automated attendance system utilizing an ESP32 camera and Python software. The system will capture images. using the ESP32 camera, process them with OpenCV, and manage the development through Visual Studio. Attendance information, including names and timings, will be stored in a Microsoft Excel file. This method ensures accurate and efficient tracking of individuals' presence. By automating the attendance process, this solution offers a cost-effective approach suitable for various organizational requirements, enhancing productivity and reliability in attendance management. The integration of image processing and data management creates a seamless and innovative system for modem attendance needs.

Specification

THE FIELD OF INVENTION
The invention pertains to advancements in the fields of automated attendance systems, facial
recognition technology, and data management. It specifically addresses the needs of
organizations for efficient attendance tracking and monitoring using a combination of
hardware and software tools.
BACKGROUND OF THE INVENTION
Traditional attendance tracking methods can be time-consuming and prone to errors. With the
development of the ESP32-based camera module and advancements in image processing
technologies, it is now possible to create an automated system that can recognize faces and
record attendance accurately. This system utilizes the ESP32 camera module, FTDI module,
breadboard, Python software, and Microsoft Excel for data storage. The ESP32 CAM,
developed by AI-Thinker, is a powerful tool that integrates Wi-Fi, Bluetooth, and a highresolution
camera, making it an ideal component for this application.
SUMMARY OF THE INVENTION
The automated attendance system developed in this project leverages the capabilities of the
ESP32 camera module to recognize faces and record attendance details. The system uses
Python for programming and Visual Studio for image processing. Attendance data, including
names and presence timings, is stored in an Excel file for easy access and management. This
system provides a reliable, cost-effective, and efficient solution for attendance tracking in
various organizational settings .
Specifications
• Components: ESP32 CAM (AI-Thinker module with Wi-Fi, Bluetooth/BLE, 32-
bit CPU, 520 KB internal SRAM, 4M PSRAM; supports UART, SPI, 12C, PWM,
ADC, and DAC), FTDI Module (used for programming, compatible with CP21 02
or CP21 04), Breadboard (for prototyping circuits without soldering), TF Card
(stores captured images).
• Software: Python (for programming the ESP32 CAM and data processing),
Visual Studio (for image processing), Microsoft Excel (for storing attendance
data).
• Operation: Assemble the ESP32 CAM, inse1t the TF card, and connect the FTDI
module for programming; program the ESP32 CAM using Python, then remove
the FTDI module; the system captures and processes images, identifie~ faces, and
records attendance details in an Excel file.
• Features: Automated time tracking (monitors entry and exit times
automatically), cost-effective (saves resources by automating attendance
tracking), ease of use (simple and efficient to operate), data storage (attendance
details stored in an Excel file for easy access and management).
DESCRIPTION
Introducing an automated attendance system using the ESP32 ~Hmera module, Python software,
and Visual Studio for image processing. This system captures and recognizes faces, records
attendance details including names and timings, and stores the information in a Microsoft Excel
file. The system is designed for ease of use and cost-efficiency, making it ideal for organizational
attendance tracking.
We Claim
I. Claim: The system efficiently records attendance by recognizing faces and storing details such as names
and presence timings.
2. Claim: The system is a cost-effective aud automated solution for tracking employee attendance.
3. Claim: The system can automatically identify and record the presence of individuals, ensuring accurate
attendance tracking.
4. Claim: The system leverages advanced image processing and data management technologies to provide an efficient attendance tracking solution.

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