Face Recognition Attendance System Using K-Nearest Neighbors Algorithm

This research paper presents the design and implementation of a real-time, contactless attendance monitoring system based on face recognition using the K-Nearest Neighbor (KNN) algorithm. With the growing need for secure, automated, and hygienic attendance systems—especially in educational institutions and workplaces—biometric solutions have become highly relevant. The proposed system utilizes a webcam to capture facial images of individuals, which are then pre-processed by converting them into grayscale and resizing them to a standard format. These images are used to train a KNN classifier that learns to distinguish between different individuals based on facial features. The system employs Haar cascade classifiers for face detection, and during execution, it continuously scans and recognizes faces in real-time, logging attendance data with timestamps. All recognized faces are stored systematically in a text-based log file organized by individual and date. The solution was tested under various environmental conditions including well-lit indoor, lowlight, and outdoor scenarios. The experimental results indicate a high level of accuracy, particularly in controlled lighting environments, with performance gracefully degrading in low-light scenarios. The simplicity and efficiency of the KNN algorithm make it well-suited for small- to medium-scale applications, offering a costeffective and scalable alternative to more complex deep learning models. This system serves as a practical demonstration of how traditional machine learning methods can still offer viable solutions for real-world biometric identification tasks.