Right: On file: Australians' faces are on file in many databases - notably on state driver licenses. Legislation to allow the use of these images as surveillance tools is currently being considered.
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Arguments in favour of Australia's Identity-matching Services Bill and the Passports Amendment Bill
1. An expanded use of facial recognition technology will help protect Australia against terrorist attacks
The Morrison government's primary motivation for an expanded use of facial recognition technology across Australia has been to reduce the likelihood on terrorist attacks.
On December 12, 2019, it was reported that the department of Home Affairs had warned the government the greater use of biometric facial recognition technology was needed to counter increasing threats of terrorism. In a detailed brief handed to Home Affairs Minister Peter Dutton - and obtained by the Australian under Freedom of Information laws - the department laid out a case for upgrading its capabilities in facial scans and said the department's IT systems are ageing and failing.
On February 12, 2018, the South Australian government produced a policy statement on the measures necessary in order to protect Australia from terrorist incursions. It stated, 'Since September 2014, there have been five attacks and 14 major disruption operations in relation to imminent terrorist attack planning in Australia. Australia is not immune to terrorists who are targeting vulnerable people, including youth, and attempting to radicalise them to commit acts of violence. Intelligence also indicates there are Australians currently fighting or engaged with terrorist groups overseas where they are gaining specialist knowledge and skills. While not all will return to Australia, any who do would pose significant threat to our safety and security.'
The policy statement further argues, 'Identity crime is a key enabler of terrorism...To combat this... the Commonwealth and other jurisdictions [need] to implement facial recognition capability. This will allow law enforcement and national security agencies to identify criminals earlier, including potential terrorists, by matching peoples' images with those on government record such as passports or drivers' licences.'
Former Australian Foreign Minister, Julie Bishop, has stated, '[These services] will help protect Australians by making it easier for security and law enforcement agencies to identify people who are suspects or victims of terrorist or other criminal activity.'
In July 2017, while announcing the introduction of facial recognition technology at Australian airports, the Minister for Home Affairs, Peter Dutton, stated, 'Australia is committed to being a world leader in the use of biometrics at our border to facilitate legitimate travel, protect our community and prevent the activities of potential terrorists...'
Defenders of the use of facial recognition technology at Australian airports have drawn attention to the number of thwarted terrorist attacks that have been attempted. It has been noted that there have been foiled several planned attacks by radicalised locals, most notably in July 2017 when federal police arrested and charged several men who attempted to smuggle an explosive device onto a plane departing Sydney Airport. The Minister for Home Affairs, Peter Dutton, has stated, 'These terrorist plots showed a very real and disturbing danger.'
Separately from the rollout of biometrics for traveller processing, the government has been building out the National Facial Biometric Matching Capability. A key part of the system, the Face Verification Service (FVS), went live in November 2016.
On March 9, 2020, Adonis Hoffman the chief executive officer of The Advisory Counsel, Inc, advised that governments in the United States, state and federal, need to adopt the type of facial recognition surveillance technology being considered in Australia. He stated, 'Our world is becoming more dangerous every day. As we stand on the doorstep of this new frontier, policymakers should err on the side of public safety...While AI cannot undo the terrorism of the past, it could mean the margin between success and failure, life and death, security and danger for us all in the future.'
2. An expanded use of facial recognition technology will help to guard against identity theft
Proponents of an expanded use of facial recognition technology in Australia argue that it will reduce the incidence of identity theft in Australia. When introducing the Passports Amendment Bill to federal parliament, the Minister for Immigration, Citizenship, Migrant Services and Multicultural Affairs, David Coleman, stated, 'The services will...contribute to preventing and detecting identity fraud...'
Identity crime is one of the most common crimes in Australia. According to the Australian Institute of Criminology (AIC), the annual economic impact of identity crime exceeds $2 billion. A survey by the AIC found that identity crime 1 in 4 Australians have been a victim of identity crime at some point in their lives.
Misuse of personal information and identity crime remains an ongoing concern for Australians, with almost all respondents to the AIC's most recent survey (96.9%) indicating that misuse of personal information was, in their view, 'very serious' or 'somewhat serious'.
Stolen and fraudulent identity credentials continue to be highly sought after by criminals, with a large amount of personal information obtained illegally online, by email, social media or through scams or data breaches. Identity crime is rarely an end in itself but is an important element in a wide range of other criminal activities. These include credit card fraud, superannuation and other financial frauds against individuals and welfare, tax and other frauds against government agencies.
Personal information is often obtained other than through the internet, with telephone and face-to-face methods being the two most prevalent methods employed. Although large numbers of identity crimes are reported officially, only a relatively small proportion of incidents result in police investigation and prosecution.
The impact of identity theft upon individuals can range from inconveniencing to highly distressing depending on how successful the identity thieves are in gaining private information and accessing sensitive accounts, such as bank accounts. The AIC study noted, 'Identity thieves can steal a person's personal identification information and access email and
bank accounts very easily. This became quite apparent to a family in Sydney, who had their mobile phone details, Facebook account, email account and bank details accessed and
changed by identity criminals within one hour.'
In an article published in Biometric.com on February 17, 2020, Sarah Amundsson, an international expert in digital identity verification, noted that governments and private businesses were increasingly adopting biometrics, including facial recognition technology, to enhance the security of those who have entrusted their data to government or corporate databases. These technologies rely on unique physical markers such as facial features, retina patterning or fingerprints to prevent fraudsters accessing valuable, private data.
Amundsson acknowledges that facial recognition technology may need to be developed even further to guard against hackers. She observes, 'As businesses rely heavily on digital onboarding, there's a need to introduce advanced AI-powered facial recognition technology that could help fight against criminals and enhancing facial recognition with 3D liveness detection provides a foolproof security solution.'
On April 24, 2019, Peter Trepp of FaceFirst noted, 'Increased accuracy using hundreds of thousands of points of measurement has made facial recognition extremely reliable.' Trepp also explained the 'liveliness' measures that are in use to prevent thieves using photographs to replicate the individual whose data they were attempting to access.
Trepp advocates for the sort of biometric indicators that Australia's proposed expansion of its facial recognition network would rely upon. He states, 'Think about how easy it is to steal a key fob, a car key, a plane ticket or a social security number. It doesn't take a great deal of skill, planning or training to pull it off. Consider how a driver's license or credit card can be replicated. Then consider how much damage can be done with that data. By contrast, biometric identifiers such as facial templates... are incredibly difficult to replicate, and biometric template keys are extremely hard to spoof. Almost anything that prevents identity theft is ultimately a victory for personal privacy.'
3. Facial recognition technology can be used to help protect the community against a wide range of conventional crimes
In addition to its benefits in preventing some types of cybercrime, such as identity theft, advocates of the proposed facial recognition technology system proposed for Australia argue that it will be of great benefit in protecting the Australian community by discouraging more conventional crime.
Proponents of the new legislation point to the successful use of facial recognition technology as a crime fighting tool in other jurisdictions. United States police have emphasised the program's benefits, with the technology having already helped authorities solve a wide range of cases, from shoplifting to child abuse. In 2017, Indiana State Police were able to utilise Clearview AI technology to identify a killer who had been caught on video shooting a man in the stomach. The killer's identity was uncovered within 20 minutes of the Clearview AI search taking place.
Currently, Amazon has given its facial recognition system to United States police departments to trial. An NBC News report published on May 11, 2019, gave an overview of some of the ways in which facial recognition technology is being used by United States police. In Colorado, local investigators foiled credit-card fraudsters, power-tool bandits and home-garage burglars and identified suspects in a shooting and a road-rage incident. In San Diego, officers snapped pictures of suspicious people in the field who refused to identify themselves. The technology has led to the capture of a serial robber in Indiana, a rapist in Pennsylvania, a car thief in Maine, robbery suspects in South Carolina, a sock thief in New York City and shoplifters in Washington County, Oregon. Currently the technology is being used as an investigative tool to help identify potential suspects; it is not recognised in courts and needs to be supplemented by other forms of evidence and police work.
Facial recognition technology is also being used by the United States Federal Bureau of Investigation (FBI). The FBI can now search databases with more than 641 million photographs. Formerly, fingerprint analysis was the most widely used biometric technology for positively identifying arrestees and linking them with any previous criminal record. Beginning in 2010, the FBI started to replace the Integrated Automated Fingerprint Identification System (IAFIS) with Next Generation Identification (NGI), which not only includes fingerprint data from IAFIS and biographic data, but also provides new functionality and improves existing capabilities by incorporating advancements in biometrics, like face recognition technology.
Agents with the FBI and Immigration and Customs Enforcement (ICE) have access to state driver's license databases and are able to scan through millions of Americans' photographs in order to detect criminals. There is regular use of facial recognition to track down suspects in low-level crimes, including cashing a stolen check and petty theft.
In 2017 the FBI used facial recognition technology to locate a gang member suspected of murder. Their apprehension of the man, later convicted of the crime, was assisted by using facial recognition technology to identify and locate his girlfriend. The crime had been committed six years earlier and the suspect was on the FBI's Ten Most Wanted Fugitives List. In January 2020, New South Wales and Victorian police confirmed that they use facial recognition technology as part of police work. A spokesperson for New South Wales Police Minister David Elliott said in a statement, 'Face Matching Services are being implemented to provide law enforcement with a powerful investigative tool to identify people associated with criminal activities.' A Victoria Police spokeswoman confirmed that Victoria Police are using a facial recognition system called iFACE to identify criminal suspects at 85 police stations.
4. Facial recognition technology can be used to prevent the spread of contagious diseases and advance public health
Since the advent of the coronavirus pandemic, advocates of facial recognition technology and its deployment by centralised agencies have noted the capacity of these systems to assist in the maintenance of public health.
A report from Reuters has indicated that facial recognition technology is currently being used to detect cases of coronavirus in China and help contain the spread of the outbreak. The report highlights one case study, indicating that authorities were able to track a resident from Hangzhou who had recently taken a trip to nearby Wenzhou - an area that has been affected by the virus - via the use of facial recognition cameras. The individual was subsequently instructed to stay indoors for two weeks.
It has also been reported that Guangzhou City is using thermometers on its city buses and employing facial recognition to scan passengers to quickly identify any symptoms of the virus. Some surveillance cameras have the ability to recognise low-grade fevers, and therefore may even be used to detect cases of the Coronavirus.The Chinese industry ministry has reportedly since sent a message to the country's AI companies and research bodies to help identify new ways of containing the outbreak. According to reports, the thermometers can scan passenger foreheads in one second, sending an alert to the driver if an anomaly is detected.
Initially, as these reports surfaced, it was thought that the face masks many are wearing as part of protective measures may hinder the facial recognition technology. However, a company in China has outlined that the technology exists to identify people who are even wearing masks.
AI-powered interactive graphs are tracking the virus' migration across China, with the company working on creating an alert system, whereby users will be able to receive information about whether an infected individual has traveled within their vicinity. These graph models are also being used by the Chinese Government to find infected individuals and provide medical resources to them.
China has also indicated how it is using this technology to track travelers. The government has tracked arrivals later suspected to pose a risk and used facial recognition technology to locate their contacts. The same technology has also been used to track and monitor individuals who were originally given a false negative after being tested for the virus.
The Chinese government has developed a monitoring system called Health Code that uses big data to identify and assesses the risk of each individual based on their travel history, how much time they have spent in virus hotspots, and potential exposure to people carrying the virus. Citizens are assigned a color code (red, yellow, or green), which they can access via the popular apps WeChat or Alipay to indicate if they should be quarantined or allowed in public.
Similarly, the Dubai World Trade Centre recently announced it was taking extra-precautionary measures to control and monitor access to its Sheikh Rashid Tower and 'ensure the wellbeing of all our tenants and visitors'. In a message sent out to its customers, it stated: 'You will be passing through the thermal cameras/scanners and/or will be referred to be medically checked by medical professionals (if required).'
In Moscow, a network of 100,000 cameras equipped with facial recognition technology are being used to make sure anyone placed under quarantine stays off the streets. The cameras are controlled from a purpose-built coronavirus control centre. Images and personal details of those under quarantine are put on a database so they can be recognised by the cameras.
South Korea has also been using surveillance cameras, mobile phone location data and credit card records to track movements of coronavirus patients.
There has also been consideration given in Australia to the use of this technology to control the spread of the coronavirus. In recent weeks, Australian transport staff have been disinfecting tap on points at public transport entry and exit points. Facial recognition could do away with the need for the touch or 'tap' element in travel entirely. Some Australian governments are considering facial recognition trials in their transport networks with a view to future use. It has been claimed that the pandemic is likely to see these plans fast-tracked.
5. Facial recognition technology is a reliable means of identification
Facial recognition technology has seen a range of advances that have largely overcome concerns regaining its accuracy.
In 2017 in his book Effective Physical Security Dr. Thomas J. Rzemyk wrote, 'Facial recognition technology has had several enhancements over the past decade post 9/11. In the mid-21st century, facial recognition was limited to characteristics related to the eyes, ears, nose, mouth, jawline, and cheek structure. Several private organizations have released updated technologies to both government and the public.
Newly enhanced technologies permit both verification and identification (open-set and closed-set). Facial recognition technology today uses complex mathematical representations and matching processes to compare facial features to several data sets using random (feature-based) and photometric (view-based) features.'
Advances in facial recognition technology are now reaching human recognition levels of accuracy. Facebook researchers are currently developing algorithms called 'DeepFace' to detect whether two faces in unfamiliar photographs are of the same person with 97.25% accuracy, regardless of lighting conditions or angles. As a comparison, humans generally have an average of 97.53% accuracy.
DeepFace creates a simulated neural network to work out a numerical description of the reoriented face to determine if there are similar enough descriptions from the two images. This network involves over 120 million parameters using locally connected layers. The DeepFace team trained the network using a dataset of 4 million facial images belonging to around 4,000 people.
These developments indicate that with expanded sources of photographic data from which to construct the database and with advances in technology automated facial recognition can operate with near-human accuracy.In an article published in TNW on February 9, 2019, Christopher Shiostu explained the conditions necessary to create highly reliable facial recognition systems. Shiostu stated, 'The accuracy of a neural network depends on two things: your neural network and your training data set. The neural network needs enough layers and compute resources to process a raw image from facial detection through landmark recognition, normalization, and finally facial recognition. There are also various algorithms and techniques that can be employed at each stage to improve a system's accuracy. The training data must be large and diverse enough to accommodate potential variations, such as ethnicity or lighting.'
It is also possible to lift the 'confidence level' of a facial recognition system so that only matches with a very high degree of correspondence are registered. A higher confidence threshold leads to fewer false positives and more false negatives. A lower confidence threshold leads to more false positives and fewer false negatives. However, raising confidence levels when judging how to act upon apparent 'matches' would help to ensure that law enforcement officers and others behaved in a manner that was appropriate to the level of confidence.
Shiostu concluded, 'Focusing on the quality and size of data used to train neural networks could improve the accuracy of facial recognition software. Simply training algorithms with more diverse datasets could alleviate some of the fears of misprofiling minorities.'
On January 27, 2020, it was reported, 'The [United States] National Institute of Standards and Technology (NIST) recently released a report that examined the accuracy of facial recognition algorithms across different demographic groups. The NIST report found that the most accurate algorithms were highly accurate across all demographic groups.'