Employee Attendance System Using QR Code and Face Recognition Using Deep Learning Technologies

Mamidi Tarani 1, Kandrapu Shyam2

1 Assistant Professor

2 MCA Final Semester, Master of Computer Applications, Sanketika Vidya Parishad Engineering College, Vishakhapatnam,

Andhra Pradesh, India

ABSTRACT

The Employee Attendance System Using QR Code and Face Recognition is a deep learning-based solution developed to automate and enhance attendance management in organizations. Traditional attendance methods are often prone to errors, time consumption, and fraudulent practices such as proxy attendance. The proposed system combines QR code technology and facial recognition to provide a secure, efficient, and reliable attendance mechanism. Each employee is assigned a unique QR code that serves as an identification token. Upon scanning the QR code, the system activates a camera to capture the employee's face and verifies their identity using deep learning-based facial recognition models. Attendance is recorded only when both QR and facial authentication are successfully validated, ensuring high accuracy and preventing unauthorized check-ins. The system maintains attendance records in a centralized database and generates reports for management and payroll processing. By integrating computer vision, deep learning, and QR technology, the proposed solution improves security, minimizes manual effort, and streamlines workforce management. The project demonstrates how intelligent automation can enhance organizational efficiency while ensuring accurate and tamper-proof attendance tracking.

Keywords: Employee Attendance System, QR Code Recognition, Face Recognition, Deep Learning, Biometric Authentication, Two-Factor Verification, Facial Embeddings, Convolutional Neural Network (CNN), dlib, OpenCV, HOG Face Detector, ResNet Embedding Model, SQLite Database, Python, Tkinter GUI, Automation, Real-Time Verification, Proxy Attendance Prevention.