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How does facial biometrics work? A visual explanation

Table of contents

In this post, we will first explain how facial biometrics engines work, and then we will conduct experiments to visualize how the Veridas engine works.

What is a facial biometrics engine?

A biometric engine is an algorithm that transforms a photo of a person’s face into a biometric faceprint.

A biometric faceprint is a set of coordinates constructed from the unique characteristics of a person’s face. In the past, engineers manually designed faceprints based on the distance between characteristic points of the face (e.g., the distance between the nose and the mouth, between the eyes, etc.), as shown in the following image:

In contrast to what we have just mentioned, nowadays, artificial neural networks can learn by themselves from databases with millions of examples and therefore, are far more accurate. Therefore, in this article we will only talk about biometrics obtained from neural networks as it is the current state of the art.

What is a biometric faceprint?

A biometric faceprint would be analogous to the coordinates we use to describe the position of a city on a map. When we train a neural network with millions of images of people, the neural network learns to transform the images into positions on a map so that pictures of the same person are close together, while images of different people are further apart. The difference between the maps we are used to and the one learned by neural networks is that the former has two dimensions, while the latter can have an arbitrary number of sizes, e.g. 512.

In the following image we can see a visual representation of this idea. In this example, images less than 100 km away belong to the same person while images more than 100 km away belong to different people. The closer the images are to each other, the more confident we are that they belong to the same person.

It is important to note that to compare the similarity between two images, it is sufficient to have the biometric faceprints. It is not necessary to store the images. In this article, we are using the images as a visual resource. Still, the correct visualization would be the following one (in which we only have the biometric faceprints).

A biometric faceprint can be represented as a numeric vector or a QR code.

Biometric faceprints are irreversible, meaning that it is impossible to obtain the person’s image from the biometric faceprint. Moreover, each biometric engine generates different and incompatible biometric faceprints. It makes no sense to compare biometric faceprints with other biometric engines because each machine learns a unique and different map.

These two facts mean that if a biometric faceprint is stolen, it is not a problem. The thief would be unable to recover the person’s face from the biometric faceprint. A new biometric would be created with a different biometric engine and the previous biometric would be useless, as each biometric model generates an entirely different map.

The image below illustrates the map that a different biometric engine would have learned. From the training “recipe” of one biometric engine, an infinite number of different biometric engines can be trained with the same level of accuracy. The first model placed the biometric faceprints on the Iberian Peninsula, while the second placed them around the Adriatic Sea.

Visualizing biometric faceprints

Labeled Faces in the Wild (LFW)

LFW is a public database for the evaluation of facial biometric engines. It contains 13233 images of 5749 different people, and 1680 people have two or more images.

We will use this database to visualize how the Veridas facial biometrics engine works.

Dimension reduction

As mentioned above, biometric faceprints are numerical vectors with many dimensions. For people to be able to visualize biometric faceprints, we need to reduce the number of dimensions to 3 (space) or 2 dimensions (plane).

Some algorithms allow us to reduce the dimensions of a numeric vector. One of the most popular is TSNE, which is based on reducing the dimension of the vectors while maintaining the distance between them. For example, in our case, we start from a biometric faceprint with 512 dimensions and the TSNE algorithm will reduce it to only 3 while trying to maintain the distances between the different biometric faceprints.

Tensorflow Projector is a Google tool that will allow us to apply dimensionality reduction with TSNE and visualize what the biometrics engine has learned during training.

The following visualization shows how the TSNE algorithm finds a representation of the biometric faceprints in 3-dimensional space. It is an iterative process that starts with a random representation and improves it progressively.

Experiment 1: Identities with more than 5 photos

For the first experiment, we will keep the people with more than 5 photos in the LFW database and, for each of these people, we will randomly choose 5 photos.

The following visualization shows how each person occupies a unique place in the space. The 5 photos of each person are so close together that it almost looks like only one photo exists. This shows us that the biometric engine is highly accurate when verifying a person against themselves, preventing someone else from impersonating us.

If we zoom in a bit, we can see (with a bit of effort because the images are so close together) that each person has 5 photos.

Finally, we can scan specific images and study which images have the closest biometric faceprint. As can be seen in the following visualization, in all cases, the 4 closest images always correspond to the same person. But it is also very interesting to see how the next closest image is already of a different person, located at a much greater distance (the distance between the images is the number next to the person’s name on the right side of the image).

In short, the biometrics engine behaves ideally: it groups all the faces of the same person at the same point in a very compact way, and never two different people share the same location.

Experiment 2: Only one image per person

In this second experiment, each person will have only one image. This will make the distance between the biometric faceprints much more significant than in the previous experiment, where there were several images of each person. Therefore, the TSNE algorithm will have to generate a representation that looks for similarities between different people.

The visualization below shows how all images are grouped into a single sphere. This behavior is ideal because it implies that the model has no relevant gender or race biases. If it did, instead of a single sphere, we would be looking at several: e.g. one sphere for men and another for women.

Finally, if we look for the closest images we can see how they are people of different race or gender in many occasions, which corroborates the absence of significant biases of the biometric model. It is also relevant to see how the numerical distance between the images is much larger than in the first experiment, which is logical because they are different people.


In the experiments carried out, we have been able to observe that:

  • Facial biometrics is not a black box: some techniques allow us to visualize and understand how it works.

  • The Veridas biometric model works perfectly, as demonstrated in the example with the LFW database: it groups all the faces of the same person in a tiny region and there is a lot of distance between different people.

  • The Veridas biometric model shows no significant bias concerning race or gender.
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