Let's Face It

Facial recognition is getting smarter, but the risks demand we proceed with caution

Sydney, Nov 30 (360info) Many smartphones click open each time we glance at the screen and some airports automate passport checks with face scans: it’s tempting to think were on the cusp of unlocking the full potential of facial recognition technology.

Visions of cutting-edge law enforcement databases, helping pick out a criminal sitting in a packed football stadium spring to mind, like a science-fiction crime film come to life.

But despite some innovations, facial recognition technology is far from ready to tackle these society-shaping challenges. This hasn’t exactly stopped states from a rush to surveillance. To understand what is holding back this technology, it is vital to understand how it works in the first place.

HOW DOES FACIAL RECOGNITION WORK?

Facial recognition is a type of image identifi cation technology. These technologies rely on many of the processes and techniques associated with artifi cial intelligence (AI). In particular, applications tend to use machine learning to classify subjects at speed and scale.

Any image recognition application starts with a ‘training & process: a powerful computer is fed a large dataset of labelled pictures and learns to recognise the characteristics associated with a subject (for instance, a car) and discern it from non-subjects (i.e. a human or a dog).

With a big enough set of diverse, labelled images, a computer will be able to tell the difference between different subgroups within the subject class (e.g. a Toyota Corolla from a Ford Focus).

The same idea applies to facial recognition: by feeding the computer enough photographs of people, it will learn how to tell them apart, effectively recognising and identifying humans at scale.

The effectiveness of any facial recognition application depends on the quality of this training process. The starting point is the dataset — that is, the stock of labelled pictures. The more pictures in the training dataset, the more accurate the facial recognition application is. But too often the training datasets contain a disproportionate amount of images of white men.

This means applications are often poorer at identifying anyone who is not a white man, such as women or people with dark skin. That inaccuracy intensifi es where two such factors are present—for example, women with dark skin.

The labelling process is also crucial to the success rate of any facial recognition application. Each picture that the AI-powered computer learns from will be accompanied by a label, which states the subject's name and other information about them. Any errors — and indeed any subjectivity — in this labelling process are then taken on by the computer, and will affect the resulting accuracy or objectivity.

The most common forms of facial recognition currently in use are one-to-one facial veri- fi cation and one-to-many facial identifi cation. Facial verifi cation involves a computer checking whether a single headshot photograph matches a different headshot picture of the same person.

It is particularly useful as a way of verifying whether an individual is who they claim to be, acting like a key or a password.

This technology is widely in use to unlock smartphones, tablets and other such devices. It is also used by some countries to verify someone’s identity at border control. Provided the core human rights protections are followed, one-to-one facial identifi cation has a relatively low risk profi le with its current usage.

One-to-many facial identifi cation is vastly different. Like facial verifi cation, facial identi- fi cation also matches a single headshot with a stored image of the subject.

However, the matching headshot with facial identifi cation will be stored somewhere in a larger database that has headshots of many others as well.

This makes the task of one-to-many facial identification much harder, and can be like fi nding a needle in a haystack.

But it’s also more useful. Facial identification doesn’t just determine whether an individual is who they claim to be. It can answer a harder question: who is this person?

A UNIQUE PROBLEM THAT NEEDS UNIQUE SAFEGUARDS

The potential application of one-to-many facial identification is limited only by one’s imagination. We have already seen dangerous displays of how facial identifi cation can unleash havoc.

In China, facial identification has been central in creating ‘social credit schemes that automatically detects and penalises citizens for petty crimes such as jaywalking.

More worrying, it has been linked to systems of control and repression of ce