Real-Time Face Recognition-Based Attendance and Employee Monitoring System Using Deep Learning

Abstract

In today’s rapidly evolving technological landscape, automation and intelligent systems are transforming traditional processes across various domains. One such area is attendance management and employee monitoring, where conventional methods such as manual registers, RFID cards, and biometric systems have limitations in terms of accuracy, scalability, and security. This project presents a Real-Time Face RecognitionBased Attendance and Employee Monitoring System that leverages deep learning and computer vision techniques to automate the process of identifying individuals and recording their presence.The system is developed using the Django web framework and integrates advanced face recognition algorithms provided by the face_recognition library, which is built on top of deep learning models. It uses a K-Nearest Neighbors (KNN) classifier trained on facial encodings to recognize individuals in real-time video streams. OpenCV is utilized for capturing video frames and performing image processing tasks.The system consists of multiple modules, including employee registration, face dataset collection, model training, real-time face detection and recognition, and attendance logging. Employees are first registered in the system, and their facial images are captured using a webcam. These images are used to train the recognition model. During operation, the system continuously captures video frames, detects faces, and matches them against the trained model to identify individuals.A caching mechanism is implemented using TTLCache to prevent duplicate entries within a short time frame, ensuring efficient attendance recording. Once a face is recognized with sufficient confidence, the system stores the detection event along with a timestamp and captured image in the database.The system provides a web interface where administrators can manage employees, view attendance records, and monitor detections based on specific dates. Compared to traditional methods, this system offers improved accuracy, reduced manual effort, and enhanced security.This project demonstrates the practical application of deep learning in real-world scenarios and provides a scalable solution for automated attendance and surveillance systems. Future enhancements may include cloud integration, mobile access, and advanced recognition models for improved accuracy.