DESIGN AND IMPLIMENTATION OF AN AUTOMATIC FACE CAPTURING DEVICE

ABSTRACT In today's dynamic landscape, where technology permeates every facet of our lives, the concept of data capture holds immense relevance. Facial capturing has emerged as a cornerstone in various fields, from security and surveillance to authentication and identification. In contemporary society, the integration of face capturing technologies has become increasingly prevalent, revolutionizing various aspects of our daily lives. However, despite significant advancements, existing capturing systems are often plagued by inefficiencies and limitations that hinder their widespread adoption and effectiveness. The primary problem our project aims to address is the need for a more robust and versatile capturing solution, specifically focusing on facial capturing and real time streaming. Addressing these challenges requires the development of an innovative solution that seamlessly integrates hardware and software components to create an automatic capturing device capable of accurately tracking faces in real-time, adapting to dynamic environments and ensuring affordability and accessibility. This project focuses on the design and implementation of an automatic face capturing device using the ESP32-CAM microcontroller, MG995 servo motors, and a pan and tilt stand. The primary objective was to capture and track faces in real-time by integrating hardware with software algorithms for face detection, capturing and positional control of the device in 3D. The ESP32-CAM is a compact and versatile camera module based on the ESP32-S chip, a powerful microcontroller widely used in IoT and embedded systems. This module integrates a high-resolution camera sensor along with the ESP32 microcontroller, offering a seamless platform for capturing images and videos while providing computational capabilities for processing and analysis. The ESP32-CAM module is equipped with features such as Wi-Fi connectivity, onboard storage, and support for various protocols, making it suitable for a wide range of applications, including surveillance, monitoring, and image recognition. Its compact form factor and low power consumption make it ideal for embedded systems where space and energy efficiency are critical considerations. The developed system, ESP32-CAM, achieved higher accuracy of 86.67% compared to Raspberry Pi systems, CCTV and Arduino-based systems. The developed system response time of 2 seconds outperforms traditional CCTV and Arduino systems. The stability of the ESP32-CAM system is high. While the system performs reasonably well in various lighting conditions, further enhancements necessary were added to match the adaptability of advanced systems and Raspberry Pi projects. Through extensive testing, the system demonstrated its ability to accurately capture and track faces. The hardware components, including the camera module and servo motors, provided reliable performance, while the face detection algorithm ensured that the face remained centered in the camera's view.