Comparative Analysis of Euclidean, Manhattan, Canberra, and Squared Chord Methods in Face Recognition

Abstract

Face recognition is currently widely used as a security component. In facial recognition, the image used will be converted into a grayish image and subsequently converted into a binary image. The binary image obtained in the next process will be analyzed. The analysis was carried out by calculating the similarity distance between the training data and the test data. In the process of measuring the distance of similarity between data sets, there are often obstacles to the implementation of complex algorithm formulas. This study solves this problem by analyzing the distance functions of Euclidean, Manhattan, Canberra, and the Squared Chord to perform facial recognition. Based on the research that has been carried out, the Euclidean distance function gets an accuracy of 58%, the Manhattan distance function gets an accuracy of 70%, the Canberra distance function gets an accuracy of 92%, and the Squared Chord distance function gets an accuracy of 66%. Based on these results, it can be concluded that Canberra's distance function with a highest accuracy result compared to the other three distance functions is better and more suitable for facial recognition.