The central insight of this new work is that even though there exist an infinite number of different possible faces, our brain needs only about 200 neurons to uniquely encode any face, with each neuron encoding a specific dimension, or axis, of facial variability. In the same way that red, blue, and green light combine in different ways to create every possible color on the spectrum, these 200 neurons can combine in different ways to encode every possible face—a spectrum of faces called the face space.

Some of these neurons encode aspects of the skeletal shape of the face—for example, the distance between the eyes, the shape of the hairline, or the width of the face. Others encode features of the face that are independent of its shape, such as the complexion, the musculature, or the color of the eyes and hair. Furthermore, the response of neurons is proportional to the strength of these features; for example, a neuron might show its strongest response to a large inter-eye distance, an intermediate response to an average inter-eye distance, and a minimal response to a small inter-eye distance. However, single neurons are not mapped onto specific nameable features. Instead each neuron codes a more abstract "direction in face space" that combines different elementary features. By measuring where a face lies along each of these different directions, the brain can then perceive the identity of the face.

http://www.caltech.edu/news/cracking-code-facial-recognition-78508