Bringing Facial Recognition Systems To Light

An Introduction to PAI’s Facial Recognition Systems Project

Facial recognition. What do you think of when you hear that term? How do these systems know your name? How accurate are they? And what else can they tell you about someone whose image is in the system?

These questions and others led the Partnership on AI (PAI) to begin the facial recognition systems project. During a series of workshops with our partners, we discovered it was first necessary to grasp how these systems work. The result was PAI’s paper “Understanding Facial Recognition Systems,” which defines the technology used in systems that attempt to verify who someone says they are or identify who someone is.

A productive discussion about the roles of these systems in society starts when we speak the same language, and also understand the importance and meaning of technical terms such as “training the system,” “enrollment database,” and “match thresholds.”

Let’s begin — keeping in mind that the graphics below do not represent any specific system, and are meant only to illustrate how the technology works.

How Facial Recognition Systems Work

Understanding how facial recognition systems work is essential to being able to examine the technical, social & cultural implications of these systems.

Let’s describe how a facial recognition system works. First, the system detects whether an image contains a face. If so, it then tries to recognize the face in one of two ways:

During facial verification: The system attempts to verify the identity of the face. It does so by determining whether the face in the image matches a specific face previously stored in the system.

During facial identification: The system attempts to predict the identity of the face. It does so by determining whether the face in the image potentially matches any of the faces previously stored in the system.

Let’s look at these steps in greater detail

A facial recognition system needs to first be trained, with two main factors influencing how the system performs: firstly, the quality of images (such as the angle, lighting, and resolution) and secondly the diversity of the faces in the dataset used to train the system.

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