This Convolutional Neural Network model attempts to detect faces in the provided image.
This uses the BlazeFace face detection model, which can be found here.
This model works by first separating images into a bunch of tiny, overlapping chunks. The pixel data from these small chunks is used to try and identify small parts of a potentially larger pattern.
Throughout this step and the following steps, the resulting data from each step of the neural network is "normalized" to always remain between 0 and 1.
Once the neural networks of the model figure out which segments of the image are likely to be part of a face, the resulting list of numbers is then downsampled by only keeping the most interesting pieces. This helps to quicken processing speed.
The model can then use the results of the previous neural layers to see if their overall results resemble what the model knows as a face. Their closeness to known patterns determines the model's predicted probability that the provided image is a face.
More detailed info about this model can be found here.