This study compares the performance of K-Nearest Neighbors (KNN) using different distance metrics—Euclidean, Manhattan, and Minkowski—as well as the standalone Euclidean distance metric in facial recognition tasks, evaluating accuracy and computational efficiency across diverse datasets including Celebrity Faces, Color FERET, Family Faces, and Yale Faces.
@article{chhetri2024decoding,title={Decoding Facial Recognition: Analyzing Standalone Euclidean and KNN Distance Metrics},author={Chhetri, Prajwol and Kshetri, Sunil Raut},journal={Journal of Science and Technology},volume={4},number={2},pages={51--57},year={2024},doi={10.3126/jost.v4i2.78952},}
