How can artificial intelligence recognize a face? This is how.
Deep machine learning, or advanced neural networks, is about a software program that is learning by itself.
The reason that it is known as “neural” or “neural network” stems from the fact that the technology is influenced by the properties of the human brains to turn data into information.
An algorithm serves training data with deep machine learning, and spits out a result. But on the way between these two, in a number of layers, the face recognition interprets the signals – hence, training data. The degree of abstraction increases by each new layer.
Say you want to develop a deep neural network that can distinguish different faces, or can calculate which faces are the same.
Face Recognition data should then be a significant number of images on the faces (the greater the dataset, the more accurate the network, in theory at least). Partially on different expressions but also on the same face with many images.
Measurements are unique to each face
First, second and thousandth time this procedure is performed by the algorithm, the result is probably not as good, but in the end the network can achieve strong results.
It can be said, in a way, that the system has learned to abstract and make assumptions from raw pixel values to the identification of faces of different individuals.
But that may not be what we people think of when we use terms like generalization: somewhat, the network has worked out a number of indicators unique to each face.
If a new picture is being served on a face to the pre-trained system, the network may match the calibration values of that face to the faces on other images.
It is called modern machine learning since several-sometimes hundred-layers can be used by such a model. There’s also a symbolic sense in such a way that we humans can’t really grasp how to identify patterns the software program does. It resides deep underneath the water, so to speak.
Even though the algorithms improve and expand as they are, there are very two other factors behind the success of deep neural networks: access to large amounts of data and cheap computing, mainly in the form of graphics cards most often associated with computer games.
It may also be kept in mind that the system described above is just one of many for classification reasons.