As it becomes more and more widely adopted, facial recognition technology has many potential applications across several industries, including health, security, marketing and retail, and robotics. Nowadays, facial recognition software is used to scan for individuals’ faces to unlock devices, search for missing children, and track down criminals on the loose. However, while facial recognition offers a wide variety of benefits, it also poses some risks that need to be addressed. New studies suggest that current algorithms are susceptible to bias and inaccurate readings. How will these findings impact facial recognition technology in the future? Let’s find out.
What Is Facial Recognition Technology?
Facial recognition is a form of technology that uses artificial intelligence to match digital images of faces against live footage or pictures of people to identify them. This technology can identify a person based on a single photo and works against the backdrop of other people in a real-time environment. Facial recognition originally came in the form of computer software. But it has also been deployed in commercial applications. That includes smartphones for ID identification and in robotics.
Most computerized facial recognition techniques measure human physical characteristics using mathematical analysis. As such, facial recognition systems are also recognized as a type of biometric identification. Nevertheless, the technology is still in its early stages. It’s also considered less effective than iris identification or even fingerprint recognition. Even then, the contactless and non-invasive processes offered by the technology makes it highly attractive to tech companies.
The Evolution of Facial Recognition Technology
Facial recognition technology has only received media attention relatively recently. But in reality, the technology has been around for some time now. Between the years 2001 and 2004, several government agencies conducted their own research using the algorithm. The algorithm was able to identify faces quickly. However, the rate of accuracy at which the computers identified the faces was still less than the rate at which humans detected and identified faces. Interest in the project would wane for six years. After that, new developments would spark interest in facial recognition.
The breakthrough that they needed finally came in 2010 with the introduction of Convolutional Neural Networks (CNNs). CNNs are a form of machine learning method. The development of CNNs, along with the availability of massive storage spaces and raw processing power, made it possible to develop face recognition technology to a level exceeding human capabilities. To learn more about machine learning, read this ultimate guide about how machine learning works.
How Facial Recognition Works
Humans find it easy to encode and recall faces without much effort. We can do it even when the faces in question change with varying facial expressions or age. While facial recognition is a no-brainer task for most people, it can be quite complicated for computers. This might have to do with the fact that whereas humans see faces for what they are, computers see faces as data points.
Most facial recognition technologies use either deep learning or machine learning software. Deep learning allows the program to learn certain objects’ characteristics from raw data. It then applies that knowledge by making decisions and identifying external data. The biggest challenge has to do with identifying real-life, three-dimensional faces that change. And its only basis is two-dimensional images as reference.
Facial recognition technology works in four stages: detection, alignment, feature scanning, and classification. The software first locks in on images of faces from an actual image or an image taken from a video. The facial recognition system will then read the geometry of the faces within the photo. It will then create a facial signature of each one. A facial signature is a combination of facial traits or landmarks that makes a face unique and distinguishable.
The facial recognition software analyzes your facial signature as a mathematical formula. It then compares them with other facial signatures already stored within the database. The computer takes into account measurements of your face. That may include the length of your forehead or the distance between your eyes. Once the software has saved a facial signature, it should be able to recognize that signature from a video or photo.
The Problem With Facial Recognition
Accuracy of Results
Facial recognition technology has blended well into different areas of modern life. But even as we feel comfortable relying on this new technology, there are many issues associated with its use. There are fears that the technology will be used for mass surveillance, racial profiling, and other violations of basic human rights and freedoms. Law enforcement bodies are increasingly reliant on technology. They use it to find potential suspects and witnesses by scanning millions of photos. Security firms also use the software to provide surveillance at public venues like concerts and schools. The software is also a potential tool to commit identity thefts online. Even the commercial industry is increasingly relying on the software to track customer behaviors and movement.
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We already know that facial recognition software needs to be fed images that it can use as a reference to match faces in real-time. But the real question here is whether it is legal or ethically permissible to take photos of individuals and feed them into a database without their consent. This is often the case for law enforcement operations.
The second issue questions the reliability of information collected via facial recognition software. If the technology being used to collect information is unreliable, who’s to say that the information collected is also reliable? Most privacy legislation also prohibits agencies from using the information. That is unless it has been proven to be accurate, up to date, complete, relevant, and not misleading. There is a lack of government regulation, international consensus, or even open discourses about what the technology can and cannot be used for leaves it vulnerable to misuse.
Implications of Algorithm Bias
What Causes Bias in Facial Recognition
The Future of Facial Recognition Technology
The impact of criticisms on the technology on its future application is not straightforward. While select organizations have abandoned the technology altogether, there are many others who continue to invest incremental resources in the technology. An example would be the security and surveillance industries which owe their success to facial recognition technology. There’s also a waiting list of industries like healthcare, education, and manufacturing seeking to delve into robotics for this purpose.
Other industries are leveraging dimensions of pattern and shape recognition including smart city initiatives. Researchers have also stated that the expected valuation of the technology will surpass $12 billion by the year 2026 compared to the $3.4 billion valuations of the industry in 2019. Companies that continue to support the technology would have to impose their own limitations on the use of technology and face responsibility for its implementation.
There are also a small group of people who are clamoring for the total ban of the technology. But this remains a minuscule proportion as most governments and big tech companies seek a middle ground. This would require the tech industry, governments, and civil society to work together to create a suitable environment for technology without harming citizens’ individual rights. The tech industry needs to improve the accuracy of facial recognition algorithms and remove or eliminate inherent biases. But then again, even if scientists can perfect facial recognition algorithms to achieve perfect accuracy, the technology still faces a myriad of ethical and legal concerns on privacy. This is where the participation of civil society organizations and governments come in. These sectors need to facilitate open discussions and develop federal regulations to ensure the technology’s ethical use across the board.
Final Thoughts on Facial Recognition
There are many ethical and legal issues surrounding facial recognition, but these are not sufficient to completely halt its progress. The algorithms available to us are only still in the infancy stages, and researchers have yet to explore their applications’ full extent. The public must allow researchers to train the algorithms further using representative and neutral data to reduce or eliminate its biases. And on this front, we depend on the tech companies who are keeping up the fight to improve it. But even in this hopeful state, it’s clear that the technology lacks direction and is in urgent need of oversight and control.
Governments and tech companies alike share the responsibility of improving technology and creating measures to ensure its proper use for responsible and ethical use. These reforms shall ultimately determine the future of facial recognition technology or even artificial intelligence in the coming years.