Facial Biometrics Driven Attendance Automation Solution Using LBPH Algorithm

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Abstract

In today’s educational landscape, managing attendance remains a central administrative task, often conducted manually on paper, consuming valuable time for educators. This project proposes a solution utilizing facial recognition technology to streamline the attendance process, thereby saving time and maintaining accurate student records.

The objective is to develop an automated attendance system that is minimally intrusive, cost-effective, and highly efficient, leveraging computer vision techniques and algorithms like Local Binary Patterns (LBP) implemented in Python. OpenCV libraries offer comprehensive functionality for operations such as face detection, training, and testing, facilitating seamless integration into the system.

The process involves two key components: face detection and face recognition. Initially, face detection identifies and captures the facial features of students, storing this data in a dataset. Subsequently, during attendance sessions, the system compares captured images with those in the dataset. Upon a match, attendance is automatically recorded, associating the student’s presence with the date and time in a CSV file.

By harnessing the power of facial recognition technology and advanced algorithms, this project aims to revolutionize attendance management in educational institutions, offering a faster, more accurate, and convenient alternative to traditional methods.

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References

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Cite as

(2024). Facial Biometrics Driven Attendance Automation Solution Using LBPH Algorithm. Journal of Open Source Developments, 11(01), 27-35