> For the complete documentation index, see [llms.txt](https://un2c.gitbook.io/un2c-wp/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://un2c.gitbook.io/un2c-wp/un2c-tech/fingerprinting-principles.md).

# Fingerprinting principles

<figure><img src="/files/ChQLKKeIt9ZridNG5Z4O" alt=""><figcaption></figcaption></figure>

As explained, different sensors can be used to obtain fingerprint digital images. The fingerprint scanner consists of an optical scanner or a capacitance scanner.

The optical scanner consists of the charge-coupled device which consists of light-sensitive diodes that give electric signals when eliminated. The tiny dots representing the light that hit the spot are recorded as pixels and the array of pixels from the image.

When we place our finger on a glass plate or monitor the surface, the camera takes the picture by illuminating the ridges of the finger. The same is possible with digital assets by integrating Image recognition technology to the UN2C software program.

#### vectorization is the conversion of <mark style="color:blue;">raster graphics</mark> into <mark style="color:blue;">vector graphics.</mark>

The task in vectorization is to convert a two-dimensional image into a two-dimensional vector representation of the image. It is not examining the image and attempting to recognize or extract a three-dimensional model which may be depicted; i.e. it is not a <mark style="color:blue;">vision system</mark>. For most applications, vectorization also does not involve <mark style="color:blue;">optical character recognition</mark>; characters are treated as lines, curves, or filled objects without attaching any significance to them. In vectorization, the shape of the character is preserved, so artistic embellishments.

<mark style="color:red;">Note that the hereafter process can be applied to identify deepfake videos of photos Vectorization is a process to get vectors structure from a collection of random pixels.</mark>

<figure><img src="/files/q5yIyjXZxRs85iHjEE9A" alt=""><figcaption></figcaption></figure>

## <mark style="color:blue;">IMAGE ANALYZATION</mark>

It involves extracting the minutiae details from the processed image and then comparing them with the already stored image patterns in the database. Minutiae extraction is done by calculating the crossing number or half of the sum of differences between pairs of pixels in an eight connected neighborhood (eight connected means a pixel surrounded by eight pixels). The cross number gives a unique identification for each fingerprint characteristic.

It has to be noted that the current method that will be described hereafter differ greatly from the current process while providing a higher level of accuracy which can be used to basically fingerprint anything without the need to use artificial intelligence to recognize patterns and the minutiae.