Tuesday, February 2, 2021

Automated Facial Recognition System of India and its Implications

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Vaishnavi Krishna Mohan

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Automated Facial Recognition System of India and its Implications


Global Views 360

Publication Date

February 2, 2021


CCTV in operation

CCTV in operation | Source: Rich Smith via Unsplash

On 28th of June 2019, the National Crime Records Bureau (NCRB) opened bids and invited Turnkey Solution providers to implement a centralized Automated Facial Recognition System, or AFRS, in India. As the name suggests, AFRS is a facial recognition system which was proposed by the Indian Ministry of Home Affairs, geared towards modernizing the police force and to identify and track criminals using Facial Recognition Technology, or FRT.

The aforementioned technology uses databases of photos collected from criminal records, CCTV cameras, newspapers and media, driver’s license and government identities to collect facial data of people. FRT then identifies the people and uses their biometrics to map facial features and geometry of the face. The software then creates a “facial signature” based on the information collected. A mathematical formula is associated with each facial signature and it is subsequently compared to a database of known faces.

This article explores the implications of implementing Automated Facial Recognition technology in India.

Facial recognition software has become widely popular in the past decade. Several countries have been trying to establish efficient Facial Recognition systems for tackling crime and assembling an efficient criminal tracking system. Although there are a few potential benefits of using the technology, those benefits seem to be insignificant when compared to the several concerns about privacy and safety of people that the technology poses.

Images of every person captured by CCTV cameras and other sources will be regarded as images of potential criminals and will be matched against the Crime and Criminal Tracking Networks and Systems database (CCTNS) by the FRT. This implies that all of us will be treated as potential criminals when we walk past a CCTV camera. As a consequence, the assumption of “innocent until proven guilty” will be turned on its head.

You wouldn’t be surprised to know that China has installed the largest centralized FRT system in the world. In China, data can be collected and analyzed from over 200 million CCTVs that the country owns. Additionally, there are 20 million specialized facial recognition cameras which continuously collect data for analysis. These systems are currently used by China to track and manipulate the behavior of ethnic Uyghur minorities in the camps set up in Xinjiang region. FRT was also used by China during democracy protests of Hong Kong to profile protestors to identify them. These steps raised concerns worldwide about putting an end to a person’s freedom of expression, right to privacy and basic dignity.

It is very likely that the same consequences will be faced by Indians if AFRS is established across the country.

There are several underlying concerns about implementing AFRS.

Firstly, this system has proven to be inefficient in several instances. In August 2018, Delhi police used a facial recognition system which was reported to have an accuracy rate of 2%. The FRT software used by the UK's Metropolitan Police returned more than a staggering 98% of false positives. Another instance was when American Civil Liberties Union (ACLU) used Amazon’s face recognition software known as “Rekognition” to compare the images of the legislative members of American Congress with a database of criminal mugshots. To Amazon’s embarrassment, the results included 28 incorrect matches.. Another significant evidence of inefficiency was the outcome of an experiment performed by McAfee.  Here is what they did. The researchers used an algorithm known as CycleGAN which is used for image translation. CycleGAN is a software expert at morphing photographs. One can use the software to change horses into zebras and paintings into photographs. McAfee used the software to misdirect the Facial recognition algorithm. The team used 1500 photos of two members and fed them into CycleGAN which morphed them into one another and kept feeding the resulting images into different facial recognition algorithms to check who it recognized. After generating hundreds of such images, CycleGAN eventually generated a fake image which looked like person ‘A’ to the naked eye but managed to trick the FRT into thinking that it was person ‘B’. Owing to the dissatisfactory results, researchers expressed their concern about the inefficiency of FRTs. In fact mere eye-makeup can fool the FRT into allowing a person on a no-flight list to board the flight. This trend of inefficiency in the technology was noticed worldwide.

Secondly, facial recognition systems use machine learning technology. It is concerning and uncomfortable to note that FRT has often reflected the biases deployed in the society. Consequently, leading to several facial mismatches. A study by MIT shows that FRT routinely misidentifies people of color, women and young people. While the error rate was 8.1% for men, it was 20.6% for women. The error for women of color was 34%. The error values in the “supervised study” in a laboratory setting for a sample population is itself simply unacceptable. In the abovementioned American Civil Liberties Union study, the false matches were disproportionately African American and people of color. In India, 55% of prisoners undertrial are either Dalits, Adivasis, or Muslims although the combined population of all three just amounts to 39% of the total population (2011 census). If AFRS is trained on these records, it would definitely deploy the same socially held prejudices against the minority communities. Therefore, displaying inaccurate matches. The tender issued by the Ministry of Home Affairs had no indication of eliminating these biases nor did it have any mention of human-verifiable results. Using a system embedded with societal bias to replace biased human judgement defeats claims of technological neutrality. Deploying FRT systems in law enforcement will be ineffective at best and disastrous at worst.

Thirdly, the concerns of invasion of privacy and mass surveillance hasn’t been addressed satisfactorily. Facial Recognition makes data protection almost impossible as publicly available information is collected but they are analyzed to a point of intimacy. India does not have a well established data protection law given that “Personal data Protection Bill” is yet to be enforced. Implementing AFRS in the absence of a safeguard is a potential threat to our personal data. Moreover, police and other law enforcement agencies will have a great degree of discretion over our data which can lead to a mission creep. To add on to the list of privacy concerns, the bidder of AFRS will be largely responsible for maintaining confidentiality and integrity of data which will be stored apart from the established ISO standard. Additionally, the tender has no preference to “Make in India'' and shows absolutely no objections to foreign bidders and even to those having their headquarters in China, the hub of data breach .The is no governing system or legal limitations and restrictions to the technology. There is no legal standard set to ensure proportional use and protection to those who non-consensually interact with the system. Furthermore, the tender does not mention the definition of a “criminal”. Is a person considered a criminal when a charge sheet is filed against them? Or is it when the person is arrested? Or is it an individual convicted by the Court? Or is it any person who is a suspect? Since the word “criminal” isn’t definitely defined in the tender, the law enforcement agencies will ultimately be able to track a larger number of people than required.

The notion that AFRS will lead to greater efficacy must be critically questioned. San Francisco imposed a total ban on police use of facial recognition in May, 2019. Police departments in London are pressurized to put a stop to the use of FRT after several instances of discrimination and inefficiency. It would do well to India to learn from the mistakes of other countries rather than committing the same.

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April 13, 2021 2:10 PM

Detecting The Ultra-High Energy Cosmic Rays With Smartphones

Smartphones have become the most commonplace objects in our daily lives. The unimaginable power that we hold in our hands is unrealized by most of us and, more importantly, untapped. Its creativity often gets misused but one can only hope that it’s fascinating abilities would be utilized. For example, did you know that the millions of phones around the globe can be connected to form a particle detector? The following article covers the CRAYFIS (Cosmic RAYs Found in Smartphones) phone-based application developed by the physicists from the University of California—Daniel Whiteson, Michael Mulhearn, and their team. CRAYFIS aims to take advantage of the large network of smartphones around the world and detect the cosmic or gamma rays bursts which enter the Earth’s atmosphere almost constantly.

What Are Cosmic Rays?

Cosmic rays are high velocity subatomic particles bombarding the Earth’s upper atmosphere continuously. Cosmic ray bursts have the highest energy compared to all forms of electro-magnetic radiation. When we say ultra-high energy particles (energy more than 1018^eV), we mean two million times more energetic than the ones that can be produced by the particle colliders on Earth.  These rays are thought to be more powerful than typical supernovae and can release trillions of times more energy than the Sun. They are also highly unpredictable as they can enter Earth’s atmosphere from any direction and the bursts can last for any period of time ranging from a few thousand seconds to several minutes.

Despite many theoretical hypotheses, the sources of these ultra-high energy cosmic rays are still a mystery to us even after many decades of their discovery. These rays were initially discovered in the 1960’s by the U.S. military when they were doing background checks for gamma rays after nuclear weapon testing. Cosmologists suggest that these bursts could be the result of super massive stars collapsing - leading to hypernova; or can be retraced to collisions of black holes with other black holes or neutron stars.

How Do We Detect Them?

When the high-energy particles collide with the Earth’s atmosphere, the air and the gas molecules cause them to break apart and create massive showers of relatively low-energy particles. Aurora borealis i.e., the Northern and the Southern lights are the lights that are emitted when these cosmic rays interact with the Earth’s magnetic field. Currently, these particles are hitting the Earth at a rate of about one per square meter per second. The showers get scattered to a radius of one or two kilometers consisting mostly of high-energy photons, electrons, positrons and muons. But the fact that these particles can hit the Earth anytime and anywhere is where the problem arises. Since the Earth has a massive area, it is not possible to place a detector everywhere and catch them at the exact moment.

Energetic charged particles known as cosmic rays hit our atmosphere, where they collide with air molecules to produce a shower of secondary particle | Source: CERN

Detecting such a shower requires a very big telescope, which logically means a network of individual particle detectors distributed over a mile or two-wide radius and connected to each other. The Pierre Auger Observatory in South America is the only such arrangement where 1,600 particle detectors have been scattered on 3,000 square kilometers of land. But the construction cost of the same was about $100 million. Yet, only a few cosmic ray particles could be detected using this arrangement. How do we spread this network around the Earth?

In addition to being cost-effective, such a setup must also be feasible. The Earth’s surface cannot possibly be dotted with particle detectors which cost huge fortunes. This is where smartphones come into the picture.

Detecting The Particles Using Smartphones

Smartphones are the most appropriate devices required to solve the problem. They have planet wide coverage, are affordable by most people and are being actively used by more than 1.5 billion users around the planet. Individually, these devices are low and inefficient; but a considerably dense network of such devices can give us a chance to detect cosmic ray showers belonging to the highest energy range.

Previous research has shown that smartphones have the capability of detecting ionizing radiation. The camera is the most sensitive part of the smartphone and is just the device required to meet our expectations. A CMOS (Complementary Metal Oxide Semiconductor) device is present in the camera- in which silicon photodiode pixels produce electron-hole pairs when struck by visible photons (when photons are detected by the CMOS device, it leaves traces of weakly activated pixels). The incoming rays are also laced with other noises and interference from the surroundings.  Although these devices are made to detect visible light, they still have the capability of detecting higher-energy photons and also low-ionizing particles such as the muons.

A screenshot from the app which shows the exposure time, the events- the number of particles recorded and other properties

To avoid normal light, the CRAYFIS application is to be run during nighttime with the camera facing down. As the phone processor runs the application it collects data from its surroundings using a camera as its detector element. The megapixel images (i.e., the incoming particles) are scanned at a speed of 5 to 15 frames per second, depending on the frame-processing speed of the device. Scientists expect that signals from the cosmic rays would occur rarely, i.e., around one in 500 frames. Also, there is the job of removing background data. An algorithm was created to tune the incoming particle shower by setting a threshold frequency at around 0.1 frames per second. Frames containing pixels above the threshold are stored and passed to the second stage which examines the stored frames, saving only the pixels above a second, lower threshold.

The CRAYFIS app is designed to run when the phone is not being used and when it is connected to a power source. The actual performance would be widely affected by the geometry of the smartphone’s camera and the conditions in which the data is being collected. Further, once the application is installed and is in the operating mode, no participation is required from the user, which is required to achieve wide-scale participation. When a Wifi connection is available the collected data would be uploaded to the central server so that it could be interpreted.

There is much complicated math used to trace back the information collected from the application. The most important parameters for the app are the local density of incoming particles, the detection area of the phone and the particle identification efficiency. These parameters are used to find the mean number of candidates (photons or muons) being detected. Further, the probability that a phone will detect no candidates or the probability that a phone will detect one or more candidates is given by Poisson distribution. The density of the shower is directly proportional to the incident particle energy with a distribution in x and y sensitive to the direction in which the particle came from. An Unbinned Likelihood (it is the probability of obtaining a certain data- in this case the distribution of the cosmic rays including their energy and direction, the obtained data is arranged into bins which are very, very small) analysis is used to determine the incident particle energy and direction. To eliminate background interference, a benchmark requirement has been set that at least 5 phones must detect and register a hit to be considered as a candidate.

It is impossible to express just how mind-blowing this innovation is. As the days pass, Science and Technology around us keep on surprising us and challenge us to rack our brains for more and more unique ways to deal with complex problems. The CRAYFIS app is simply beautiful and it would be a dream-come-true to the scientists if the project works out and we are able to detect these high energy, super intimidating cosmic rays with smartphones from our backyard.

Further Reading

The paper by Daniel Whiteson and team can be found here.

An exciting book “We Have No Idea” by Daniel Whiteson and cartoonist Jorge Cham can be found here.

The CRAYFIS app can be found here.

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