Facial Recognition-Based Attendance System

Abstract

Facial recognition stands as one of the most efficient applicationsinimage processing, playing a crucial role in the technical sphere. Identifying human faces isa pressingconcern, particularly in verifying student attendance. Utilizing facial biostatistics, an attendance system employing face recognition relies on high-resolution monitoring and advanced computer technologies. The objectiveofdevelopingthissystemistodigitize the traditional method of attendance-taking, which involves verbal calls and manual record-keeping. Current attendance procedures are laborious and time-consuming, prone to manipulation through manual recording. Both traditional attendance marking and existing biometric systems are susceptible to fraudulent proxies. This paper aims to address these challenges. The proposed system incorporatestheHaarcascadealgorithm,OpenCV, Dlib, Pandas, and MySQL. Following facial recognition, attendance reports are generated and saved in Excel format. The system undergoes testing under different conditions, such as variations in illumination, head movements, and changes in camera-to-student distance. Rigorous testing evaluates overall complexityand accuracy. The proposed system proves to be an efficient and robust solution for classroom attendance management,eliminatingmanuallabourandtime consumption. Additionally, the system\'s developmentiscost-effectiveandrequiresminimal installation.

Introduction

This project focuses on a facial recognition-based attendance system, which offers a more accurate, efficient, and secure alternative to traditional methods like manual entry, RFID, and fingerprint scanning. By analyzing real-time classroom video, the system automatically detects and matches student faces to a pre-registered image database, significantly reducing proxy attendance and manual effort.

Key Features and Methodology

1. Applications:

   • Useful in education, forensics, smartphone security, and aiding the visually impaired.

2. System Functionality:

   • Face detection, segmentation, and recognition are the core stages.

   • Captured video is processed to identify and match faces using machine learning.

   • Attendance is updated in real time, and absence reports can be automatically emailed.

3. Algorithms Used:

   • Haar Cascade Classifier: Detects faces.

   • LBPH (Local Binary Pattern Histogram): Recognizes faces based on texture.

   • Eigenfaces & Fisherfaces (LDA): Dimensionality reduction and class separation techniques.

4. Technologies:

   • OpenCV for image processing.

   • Python as the main programming language.

   • Pandas for data handling.

   • SQL for database management.

   • Visual Studio Code for development.

   • Excel for data reporting.

Advantages

• High accuracy in varied lighting, angles, and facial expressions.

• Contactless operation, enhancing hygiene and convenience.

• Efficient automation reduces time and administrative overhead.

Future Scope

• Integration of AI & Deep Learning: Improved accuracy in complex scenarios.

• Predictive Analytics: Forecasting attendance trends.

• IoT Integration: Automated check-in via sensors and smart devices.

• Security Enhancements: Used for access control and HR/payroll integration.

• Continuous Learning: The system evolves and improves over time.

Conclusion

The Attendance Management System, powered by facialrecognition,provides students withquickand convenient access to theirattendance data, which is subsequently transferred to an Excel spreadsheet. the proposed system offers improved facial recognition process that is not dependent on any external devices like raspberry pi or any other moduleforcapturingfacesandrecordingattendance [1]. This system offers technology with improved security and user friendly interface.

References

[1] A. Arjun Raj, M. Shoheb, K. Arvind and K. S. Chethan, \"Face Recognition Based Smart AttendanceSystem,\"2020InternationalConference on Intelligent Engineering and Management (ICIEM), London, UK, 2020, pp. [2] H. Yang and X. Han, \"Face Recognition Attendance System Based on Real-Time Video Processing,\" in IEEE Access, vol. 8, pp. 159143- 159150, 2020. [3] Edison Kagona and Sani Usman \"Student’s AttendanceManagementinhigherinstitutionsusing azure cognitive service ad Open CV face detection & recognition attendance system” Department of Computer Science & Information Technology, Faculty of Science & Technology, International UniversityofEastAfrica;P.O.Box35502Kampala. [4] Smitha, & Hegde, Pavithra & Afshin,. (2020). Face Recognition based Attendance Management System. International Journal of Engineering Research and. V9. 10.17577/IJERTV9IS050861. [5] Sandhya Potadar , Riya Fale , Prajakta Kothawade , Arati Padale, 2021, Attendance Management system using Face Recognition, INTERNATIONALJOURNALOF ENGINEERING RESEARCH& TECHNOLOGY [6] (IJERT)Volume10,Issue08(August2021). [7] M. Shamila1, Bhanu Prakash, Asrar Ahmed, Poshak Prajeet, Ruby Pant ”Smart Attendance Autiomation System” Department of CSE (AIML), GRIET, Hyderabad, India Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India. [8] Ashwin Rao “AttenFace: A Real Time Attendance System Using Face Recognition” InternationalInstituteofInformationTechnology,Hyderabadashwin.rao@students.iiit.ac.in [9] Dr.SaritaSanap,Ms.SakshiNarwade,Mr.Sahil Goge, Mr.KrishnaPandit“FaceRecognitionBased Attendance System Using Histogram of Oriented Gradients and Linear Support Vector Machine” Assistant Professor, Department of ETC Engineering,MIT,Aurangabad,Maharashtra,India. [10] S. Dev and T. Patnaik, \"Student Attendance SystemusingFaceRecognition,\"2020International Conference on Smart Electronics and Communication(ICOSEC),Trichy,India,2020,pp. 90-96. [11] Mihir Ghanekar, Archita Sehgal, Shrishail Gouraond, Maitray Wani, Prof. Pramila M “Facial recognition based attendance system” International Research Journal of Engineering and Technology, Dept of Computer Engineering and IT, Mumbai, Maharashtra, India. [12] Salim, Omar Abdul Rhman, Rashidah Funke Olanrewaju, and Wasiu Adebayo Balogun. \"Class attendance management system using face recognition.\" 2018 7th International Conference on Computer and Communication Engineering (ICCCE). IEEE, 2018. [13] Siswanto, Adrian Rhesa Septian, Anto Satriyo Nugroho, and Maulahikmah Galinium. \"Implementation of face recognition algorithm for biometrics based time attendance system.\" 2014 InternationalConference on ICT ForSmartSociety (ICISS). IEEE, 2014. [14] Sen,S.,Patel,M.,Sharma,A.K.(2021).Software Development Life Cycle Performance Analysis. In: Mathur, R., Gupta, C.P., Katewa, V., Jat, D.S., Yadav, N. (eds)Emerging Trends in Data DrivenComputingandCommunications.Studiesin Autonomic, Data-driven and Industrial Computing. Springer, Singapore.

Copyright

Copyright © 2025 Monil Shah, Mehmood Khan, Krish Shah, Kshitij Patil. 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.