 # Identity Technology – It’s Really Just Math

The field of biometrics is the measurement and analysis of unique physical characteristics (such as fingerprint or voice patterns) especially as a means of verifying or identifying a person. In the case of facial recognition, a feature set, called a template, is extracted from the image of the face during the enrollment process and stored in a database instead of the actual image. Templates are very small compared to images so comparing templates is faster and less computationally intensive as comparing images. Also, templates can never be converted back into a photograph, so working with templates is more secure than working with photographs. Here we’ll discuss the basics of what templates are the advantages of their use.

### What is a Template?

A template is a mathematical structure that represents elements as linear transformations of vector spaces. These will be unique from person to person. During identification, the neural net templatizes the probe and proceeds to perform a 1:N comparison. This means that the probe template is compared to every template in the database. Similarity scores are computed and using a threshold the top scoring enrollment is returned as the probable identity.

For an example we’ll look at a CNN (convolutional neural network). These networks follow the common example of the human brain in the way that each “neuron” receives an input and based on some weights and biases spits out an output. What is being done here behind the scenes is the computation of the scalar product (plus a couple other functions). Here is a visual example that demonstrates the process. The scalar product is computed by taking vectors of equal dimensionality, multiplying two values of the same dimension and determining the sum over n total dimensions of the vector space. These neurons are connected together in methods defined by the layer. A layer is a collection of neurons that cooperate at some specified depth in the network. To give a simple layer by layer description, first the neural network will take in an image of bounded width and height and split the image into the three RGB color channels. 