Face Recognition Using K-Nearest Neighbors Classifier In Machine Learning

Facial recognition technology has become integral to numerous security and authentication systems. This study introduces a facial recognition system based on the K-Nearest Neighbors (KNN) algorithm. The system ensures reliable identification by utilizing a preprocessed dataset and Haar-cascade classifiers for facial detection. Training the KNN model resulted in an impressive 95.21% accuracy on a real-time dataset encompassing multiple individuals’ faces. Across all evaluated classes, the model consistently achieved 95.21% accuracy, along with a recall of 0.95 and an F1score of 0.95, demonstrating its robustness and effectiveness. The technology can significantly improve security and authentication systems in both developed and developing countries, enhancing social protection systems and contributing to greater equality and inclusion. This approach provides a sustainable and efficient solution to safeguarding access to basic services for vulnerable and disadvantaged populations. a recall of 0.95 and an F1-score of 0.95, demonstrating its robustness and effectiveness