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KNN Algorithm: A non-parametric regression and classification algorithm based on the model structure generated from data without any assumptions of its own.

KNN is used for measuring similarities by vector representation and comparison using an acceptable distance metric in various domains of data processing, pattern recognition, and intrusion detection. KNN is called memory-based or lazy learning in light of the fact that the manner in which it learns is simply storing representations of the training examples. An object is classified depending on the majority votes of its neighbors in the training set. The new model item will be ascribed to the class with its most comparable K-Closest Neighbors.

For facial acknowledgment, we can select the face descriptors and use the K-Nearest Neighbors (KNN) calculation to train our classifier.

Euclidean Distance Function of K-Nearest Neighbor can be used for feature extraction: