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Proceedings of the 2024 Ninth International Conference on Research in Intelligent Computing in Engineering

Annals of Computer Science and Information Systems, Volume 42

Implementation of a Facial Recognition System for Attendance Tracking Utilizing the K-Nearest Neighbors Algorithm

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DOI: http://dx.doi.org/10.15439/2024R54

Citation: Proceedings of the 2024 Ninth International Conference on Research in Intelligent Computing in Engineering, Vijender Kumar Solanki, Tran Duc Tan, Pradeep Kumar, Manuel Cardona (eds). ACSIS, Vol. 42, pages 157–161 ()

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Abstract. In today's digital age, facial recognition systems are crucial across industries for authentication, security, and identity verification. While slightly less precise than iris or fingerprint recognition, facial recognition's non-invasive nature fuels its growing popularity. It's extensively used for attendance tracking in various institutions, replacing error-prone manual processes. The proposed framework involves four stages: attendance updating, face detection, recognition, and database construction, employing techniques like Local Binary Patterns and Haar-Cascade classifier. Notably, in the recognition stage, the K-Nearest Neighbors (KNN) algorithm plays a pivotal role. KNN aids in accurately identifying individuals based on facial features, ensuring precise attendance tracking. Attendance records are then emailed to relevant faculty members at the end of each class, streamlining administrative tasks.

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