From enforcing COVID-19 quarantines to analysing protesters attending political rallies, facial recognition technologies have found usage beyond commercial interests. Globally, governments are high on investing in the adoption of facial recognition technologies, especially in China and the US.
On Friday, Moscow’s metro network launched a fare payment system using facial recognition technology at its 240 stations. For a city with 12.7 million people, it has one of the world’s largest video surveillance systems. Commuters have the option to submit their picture, link it to their transport and bank cards and use ‘Face Pay’ for payment of the fare. Recently, Australia too expanded a program leveraging facial recognition to enforce COVID-19 safety precautions.
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According to a report by MarketsandMarkets, the global facial recognition market size is expected to grow to USD 8.5 billion by 2025. China has the most extensive public surveillance system, making it the biggest buyer of CCTV cameras (626 million by 2020) and facial recognition technologies. Use cases such as public security, biometric sign-in, healthcare services, and eLearning platforms, among others, are expected to be deployed at a large scale. The technology even helps governments solve various criminal investigations and rapidly identify offenders.
The Pandemic Waves
During the pandemic situation, contactless verification technologies such as facial recognition became highly important. However, since facial recognition relies on the data points on a person’s image and face-masks block a lot of identifying information, the algorithms started failing. Already, the algorithms could be fooled with bad angles and improper lighting; masks made matters worse. In early 2020, the face recognition technology registered error rates between 5% to as high as 50%.
Tech giants like Apple got to developing algorithms that work while people are wearing masks. Initially, they came up with an update that could detect a mask and prompted the user to enter their passcode. Japan’s NEC in January announced a system that was 99.9% accurate over masks.
|The Development of the Facial Recognition technology since 1960Facial recognition technology has gone over numerous changes since its inception. Coined in 1960, identities were automatically differentiated based on the manual marking of various “landmarks” on the face, like the placing of the eyes and the mouth. Later, the work was extended and standardised to include 21 specific subjective markers like hair colour and lip thickness to automate the recognition. In the late 80s, scientists applied linear algebra to the problem of facial recognition and formed the Eigenface system. It was in the early 1990s when development for the technology for commercial uses was initiated. In 2006, the US government supported the Face Recognition Grand Challenge (FRGC) to promote and advance face recognition technology. Here, 3D face scans, high-resolution face images, and iris images were used in the tests to make the technology 100 times more accurate.|
It was not until 2010, when the consumer experienced face recognition technology that was introduced by Facebook to identify people whose faces featured in the photos of their users. The major breakthrough that we see now happened when Apple launched the iPhone X that could be unlocked with FaceID. Post that, the technology is being used by airlines, airports, border controls, stadiums, transport hubs, mega-events, concerts, and conferences, among others.
Errors can make lives miserable
While it sounds like the go-to technology for biometrics, experts worry that with more and more governments using it, the drawbacks and failures of the technology can prevent needy people from getting benefits. Also, facial recognition has been proved to be less accurate for people of different races. While tech companies promise a 90-95% accuracy rate, they claim that the government human resources only need to take care of the remaining 5-10%. This is also where experts worry, as most governments worldwide don’t consider 5% as an issue.
Not just governments but also private companies are unable to handle the situation that comes with the failures of the technology. Two unions have taken legal action against Uber, alleging that the firm has unfairly dismissed drivers based on their racially biased software used to verify drivers’ identities. In fact, the Independent Workers’ Union of Great Britain (IWGB) has also asked Uber to completely scrap the use of the technology that causes indirect racial discrimination.
Experts believe that if AI, in its automated decision-making, learns discriminatory biases, then the whole purpose of technology fails. Unchecked face recognition tools carry the potential to further push away the marginalised groups.
Facial recognition is also not privacy-preserving. Face Ids are personally identifiable information (PII) that need permission to collect, store and process. This is especially under the General Data Protection Regulation (GDPR) Act, and many people might not choose to use facial recognition.
One of the limitations that technical experts are working on is the ability of the face recognition algorithms to recognise changes in facial appearances throughout a person’s life from childhood to old age. The pandemic has made it quite clear that facial recognition technology is here to stay and will even advance further.