Face Matching Using the Euclidean Distance Algorithm
Explore how to implement face matching by calculating the Euclidean distance between facial encodings in Python. This lesson guides you through intercepting faces, extracting feature vectors, and comparing them against a database to determine similarity scores. You will understand how smaller distance values indicate higher matching accuracy and how to build a utility to perform face recognition efficiently.
Introduction
The Euclidean distance is an efficient and straightforward distance measurement method that is adequate for calculating face similarity. Basically, the distance between two face images reflects the degree of similarity between these images. A coherent calculation of the distance between face images may further optimize the face recognition process and improve its accuracy.
What is the Euclidean distance metric?
The Euclidean distance, also known as the L2 distance, represents the shortest distance between two points. This metric, commonly used in many applications, can be used to measure the distance between two points in a two-dimensional space. The computation of the Euclidean distance can be generalized to calculate the absolute distance between two points in the case of n-dimensional space. The following figure is a visual illustration of the Euclidean distance metric:
In two-dimensional Euclidean space, the distance between two points is given by the following formula:
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