FaceSort: An Automated Image Organizer Using Facial Recognition

Abstract

This work introduces a practical system designed to perform facial recognition in real time, paired with automated email delivery for streamlined communication. The system is built using Python, making use of libraries such as OpenCV and face-recognition to capture and analyze facial features from a live webcam feed. Once a face is identified, the captured image is sent to a cloud- based service for classification. After processing, the system automatically emails the classified image to a specified recipient using secure email protocols. By automating these tasks, the system minimizes manual effort, improves reliability, and ensures that important information is delivered quickly and efficiently.

Introduction

Facial recognition technology is increasingly vital for identity verification, access control, and personalized interactions across many sectors such as security, healthcare, education, and customer service. This project develops a comprehensive facial recognition system combining real-time face detection, cloud-based image classification, and automated email delivery. Built with Python tools like PyCharm, OpenCV, and face-recognition libraries, it processes faces from live webcam feeds or uploaded images, then sends personalized email notifications automatically, eliminating manual intervention.

The literature survey reviews diverse facial recognition methods and applications, including algorithms like Local Binary Pattern Histogram (LBPH), Principal Component Analysis (PCA), and deep learning models such as CNNs. It highlights practical implementations using Amazon Rekognition and Haar Cascades, discusses challenges like varying lighting, occlusion, and rapid movement, and explores integrations with communication tools like WhatsApp and email for enhanced automation and security. Recent advances emphasize scalable cloud-based solutions, ethical considerations, and improving accuracy with hybrid and deep learning techniques.

Overall, the project and reviewed studies demonstrate the potential of integrating real-time face detection with intelligent processing and automated communication to create efficient, user-friendly, and secure facial recognition systems adaptable to various real-world applications.

Conclusion

In an era where digital efficiency and intelligent automation are increasingly essential, this facial recog- nition system offers a practical and forward-thinking solution. By integrating real-time face detection, cloud-based image classification, and automated email communication, the system eliminates the need for manual intervention while ensuring speed and accuracy. Developed with reliable Python tools and libraries, it demonstrates how emerging technologies can work together to enhance identity verification and stream- line communication across various industries. Ultimately, this project not only simplifies facial recognition processes but also sets a strong foundation for future enhancements in smart, automated systems.

References

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Copyright

Copyright © 2025 Mrs. Radhika S N, Anvitha H M, Chethana T, Impana T S, Apeksha K K. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.