RegTech: The Case for Financial Inclusion

RegTech: The Case for Financial Inclusion

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The FinTech industry has grown tremendously in recent years, introducing both scale and efficiency in new banking technologies. According to Statista, at an annual growth rate of 18%, the global transaction value in FinTech is expected to grow to $8 trillion by 2022. But according to research by Thomson Reuters, the financial industry spends at least a day, weekly, to track regulatory shifts which can be increasingly time-consuming and costly. So as the fintech and compliance costs continue to grow, RegTech seems to be getting global attention from the financial sector, as well. 

Traditional financial service providers (banks, insurance, transactions, and payment services, mobile wallet payments) have no option but to catch up with changing tides, in order to survive the technology revolution. From cutting costs to providing seamless transaction experiences, FinTech and RegTech both have changed the way individuals and businesses manage money. 

The world has witnessed more transparency in banking, and, financial transactions are thriving with the use of disruptive technologies such as AI, machine learning, and blockchain. Fueled by the advent of the internet, FinTech has now grown to taller heights with mobile payments and online banking. 

Reaching the Unbanked

One of the marked success of this digital wave is how it has led to increased access for previously unbanked populations, largely due to mass outreach of mobiles and the internet. Now, mobile phones are making it possible for more and more people to enter the global financial system, albeit with limited access to services such as mobile payments and transfers. 

The mobile money market is witnessing a revolutionary transformation fueled largely by:

  1. Growing focus on customer experience
  2. Diversified financial services structure
  3. Evolving regulatory landscape
  4. Expanding mobile money services

Mobile money accounts, as well as text and app-based financial accounts, are providing financial coverage to growing global populations. A small but rising percentage is also taking advantage of smartphone technology around the world. However, this is subject to the availability of adequate underlying infrastructure such as power supply. The challenge is greater in developing countries where only 40% of adults have access to both the internet and mobile phones, as opposed to 82% in high-income economies. 

Online Security and meeting global compliance is still a topmost priority for customers and businesses alike. For this reason, digital banks are also focusing on RegTech in banking solutions for building long-term trust in the market.

How RegTech and FinTech are related

A large customer base is currently left unserved in the financial services industry due to a lack of the right infrastructure. As the FinTech revolution continues to benefit the economy and break into new markets, it promises to close gaps in financial inclusion. However, this comes with high risks of exploitation that need to be managed. 

Currently, 1.7 billion people in the world are unbanked, down from 2 billion in 2014. This is one of the most challenging pain points for financial service providers. FinTech is changing this, and RegTech can accelerate the process.


How RegTech is Relevant to FinTech


RegTech startups are experiencing growth and investment at almost the same rate as the FinTech industry. Firms are realising the need to capitalize on compliance efficiency and use it for a competitive edge in the industry. There is great potential for powering the future of financial regulation by integrating technologies into supervisory systems used by banks.

RegTech has major implications for financial institutions in the form of reduced regulatory costs and improved operational efficiency. With far-reaching benefits for the economy, RegTech in banking is also aspiring to drive growth and profitability by better regulatory reporting and risk management, as well as transaction monitoring. 

This is especially relevant for emerging markets, where a notable percentage of the population can experience compounded benefits from access to services like micro-credit and remittances. The effective use of RegTech in banking strikes a balance between access to credit and credit security. 

With machine learning, artificial intelligence, and e-KYC (Know-Your-Customer) verification methods, the gains are far-reaching. Fraud mitigation and reduced compliance costs make it possible for FinTech to include more financially excluded population segments. Automated KYC processes through RegTech ensure that foolproof methods for legal use of financial services can be made effective. Using API code, RegTech can also simplify complex regulations that optimise compliance costs of time and labor. 

Both financial institutions and regulatory authorities see added value in the adoption of RegTech for better compliance and service delivery. APIs for data collection and reporting have also shown a marked improvement in customer engagement, as well as compliance. 

RegTech solutions and AML compliance

RegTech solutions are increasingly used by financial institutions to comply with the regulation of anti-money laundering and the evolution of other financial crimes. There is no denying the fact that eliminating the crimes of money laundering has been one of the biggest challenges for financial institutions over the years when new and improved methods of money laundering are on the rise. But regulatory technology (regtech) is helping financial institutions to eliminate financial crimes through regular AML checks, set into motion by regulatory authorities. 

Regtech solutions for AML compliance offer a cost-saving solution to the financial sector for real-time identity verification, crime monitoring, and reporting. It not only improves the efficiency of the entire system but also reduces operational costs altogether. 

With the use of intelligent technologies, RegTech in banking is a frictionless solution that can reduce time by easily screening people against vast databases. The regulatory landscape is subjected to regular change, this evolution of regulatory trends affect the business operations directly. That’s why RegTech solutions and AML compliance is the need of the hour. 

Service offerings by RegTech

Driven largely by business demand and technology innovation, there are five main service offerings by RegTech :

  1. Regulatory reporting
  2. Risk management
  3. Identity management and control
  4. Compliance
  5. Transaction monitoring

Challenges in Financial Service Delivery 

As financial services become more digitized and pervasive, regulatory systems need to adopt more forward-thinking ways of digital transformation. 

The foremost challenge in providing digital financial services to previously unserved populations is risk management. In most cases, financial authorities are still learning their way into the digital revolution. If vast amounts of data are collected without apt use of APIs, serious data security concerns could arise. This could undermine the regtech revolution and make the onboarding process more complex for new entrants.  

Supporting infrastructure in the form of digital databases is also absent in most cases. While there is a steep demand for mobile money accounts, some key services such as government payments (pensions, wages, social benefits) are still paid in cash. This reinforces financial exclusion for large segments of the population who could otherwise benefit from services such as mobile payments. 

Additionally, stringent identity verification requirements, such as those in KYC, get in the way of digital relevancy. National identity document verifications are sometimes not enough to ensure that people from remote areas can open an account and other local documents are required for account opening. This opens up a range of opportunities for the RegTech industry to influence financial service delivery, and in turn financial inclusion. 

RegTech Solutions: Closing Delivery Gaps

Across the globe, traditional financial systems are increasingly embracing technological advancements and committing to streamlining regulatory networks. Regulatory sandboxes and ‘reg labs’ are now being facilitated for innovation, to cater largely to the spike in RegTech solutions and AML compliance in both developed and developing countries. 

Sandboxes are controlled spaces for tech firms to test out new technologies under the regulator’s supervision. In addition to offering room for innovation, RegTech sandboxes can also be used as effective feedback and communication channels between FinTechs, regulators, and RegTech solution providers. For financial inclusion, this means balancing innovation and risk to reach underserved customers. 

Improving access to mobile money markets also depends a great deal on the efficient implementation of KYC regulations. In areas where access to financial services is a challenge, fulfilling tedious document verification requirements can be a cumbersome task. This stands in the way of scaling mobile money networks, hence hurting financial inclusion. 

This is where RegTech plays a central role. By simplifying customer onboarding processes, through efficient use of AI and HI, the mobile money industry can get a real push. The use of innovative e-KYC technologies such as biometric authentication and digital ID systems can make the process more efficient. 

With tangible results in the form of financial stability and customer engagement, investment in better regulation technology is being recognised as key to an efficient financial system. A sound regulatory environment, with regtech applications that support risk management, will ensure that economies reap maximum value from the FinTech revolution.

How machine learning changed facial recognition technology?

How machine learning changed facial recognition technology?

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We are entering a new era of fast and secure authentication clubbed with a perfect storm of digital transformation. The convergence of AI and biometrics is a part of this transformation and as with many technological breakthroughs, this transformation is not down to the advancement in a single technology. 

The implementation of face recognition has seen many iterations starting from its origin in the 1960s when it was manually implemented with a RAND tablet (a graphical computational device). The technology was incrementally refined during the last century. However, adoption of facial recognition on a large scale became possible, thanks to the breakthrough of deep machine learning in the early 2010s. In this article, we will try to elaborate how machine learning has changed facial recognition technology and the impact it has on the development of robust authentication systems.

The facial recognition market is expected to grow to $7.7 billion by 2022 and it’s because the technology has all kinds of commercial applications. From airport security to healthcare and customer authentication, face recognition is now widely adopted around the globe. 

What part does machine learning play in face recognition mechanism?  

Deep machine learning or deep neural networks are about a computer program that learns on its own. The fact that it is called “neural” or “neural network” comes from the basis that the technology is inspired by the human brain’s properties to transform data into information. It is a variant of the more general concept of machine learning, which in turn is part of a more comprehensive concept called artificial intelligence.

With deep machine learning, an algorithm serves training data and delivers results. But in between input and output, the algorithm interprets the signals – i.e (training data) – in a number of layers. For each new layer, the degree of abstraction increases.

Say, you want to build a deep neural network that can differentiate different faces or that can determine which faces are identical. Training data should then be a large number of images of the faces. The larger the dataset, the more accurate the network, at least in theory.

A computer does not “see” a face in the image, but several values ​​representing different pixels. With the pixels as a background, the deep neural network learns to find patterns. For each layer passed in the network, some patterns become more interesting (stronger signal between the “neurons” in the network) while others are nonchalant (weaker signal). During training, the “weights” of the various signals are varied to produce the desired result better and better.

The first, second and hundredth time the algorithm performs this procedure, the results are usually not as good, but eventually, the network can achieve impressive results. In a way, one can say that the network has learned to abstract and generalize, from raw pixel values ​​to the classification of different people’s faces.

But that is perhaps not what we humans think of when we use terms such as generalization; it is rather the network has worked out some metrics that are unique to each face. If the pre-trained network is served a new image on a face, the network can match its measurement values ​​to the faces on other images. If the network generates roughly the same values ​​for different images, it is likely the same person on both images.

It is called deep machine learning because such a model can use multiple – sometimes a hundred layers. This is symbolic; humans cannot understand how the computer program finds patterns. It operates in different layers.

Although the algorithms are developed and refined as they are, there are two other reasons behind the breakthrough of the deep neural networks: access to large datasets and cheap computing power, especially in the form of graphics cards that were most often associated with computer games.

It may also be borne in mind that the method described above for classification purposes is only one of many but is commonly used. 

Anti spoofing techniques for face recognition

While converging machine learning algorithms with face recognition makes it more accurate and fast, there is another feature that makes machine learning a must-have for face authentication – Anti Spoofing. This innovative technology shows a lot of promise and has the potential to revolutionize the way we access sensitive information.

Even though face recognition is promising, it does have some flaws. Simple face recognition systems could easily be spoofed by using paper-based images from the internet. These spoof attacks could lead to a leak of sensitive data. This is where the need for anti-spoofing systems come into play. Facial anti-spoofing techniques help prevent fraud, reduce theft and protect sensitive information.

Presentation attacks are the most common spoofing attacks used to fool facial recognition systems. The presentation attacks are aimed to subvert the face authentication software by presenting a facial artefact. Popular face artefacts include printed photos, replaying the video, 2D and 3D facial masks.

AI-based anti-spoofing technology has the ability to detect and combat facial spoofing attacks. With features like 3D depth perception, liveness detection, and microexpression analysis, our deep learning based facial authentication system could accurately analyze the facial data and identify almost all kinds of spoofing attacks. Shufti Pro detected 42 different spoof attacks in 2019. Among these 3D face mask attacks were in high volumes – almost 30%.

Machine learning-based presentation attack detection algorithms are used to automatically identify these artefacts to improve the security and reliability of biometric authentication systems.

Machine learning-based face verification systems rely on 3D liveness detection feature for successful detection of spoofing attacks including 3D photo masking, deep fakes, face morphing attacks, forgery, and photoshopped images. Liveness detection verifies whether a user is present or is using a photo to spoof the system.

What to expect in the future for facial recognition?

The human face has already become a perfect means of authentication and will have more impact on the digital transformation in the future. By using the face as an identifier, we are already able to open an online account, make online payments, unlock the smartphones, go through checking control at the airport or access medical history in the healthcare sector. 

In general, facial biometric technology has widespread potential in four categories: law enforcement and security, online marketing and retail sector, health sector, and social media platforms. AI-empowered face recognition technology has the potential to become predominant in the future. 

One of the future implications of technology is identifying facial expressions. Detecting emotions with the help of technology is a difficult task but machine learning algorithms have the promising potential to automate this task. By recognizing facial expressions, businesses will be able to process images and videos in real-time for better monitoring and predictions hence saving the cost and time.

Although it’s hard to predict the future facial recognition technology with the rapid growth and adoption of technology, it will become more widely adopted across the globe with more sophisticated features.

AI face recognition for total automation

AI face recognition for total automation

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Face recognition is everywhere but still we’re unable to say goodbye to document, maybe because we’re still far away from total automation that we wish to achieve with this technology. 

A research stated that the global facial recognition market is expected to grow at Cumulative Annual Growth Rate (CAGR) of 16.6% reaching a record-high value of USD 7.0 billion by 2024. This growth is quite justified as we see face recognition everywhere in our day to day life. 

Nations such as U.S., U.K, EU, China, etc are leading the convoy of these technological advancements where one’s face will replace the identity documents. Face verification solutions are used everywhere, from our mobile phones, and Facebook, to crime control agencies and airports. But this awesome technology has unique limitations and benefits for different industries where it’s used, that will be explored in this blog. 

Let’s explore some use-cases of face recognition. 

Travel is becoming document-free

An increasing amount of airports are using face verification solutions to verify the identity of their passengers. As airports are commonly used for huge crimes such as human trafficking, money laundering, and drug trafficking. The documents are losing their touch which leads the airports to use advanced technologies for passenger screening. Passenger security and their travel experience are equally significant and face verification satisfies both these requirements. Accuracy rate as high as 98.67% is achieved with facial recognition and results are delivered within 15-60 seconds.  It enhances customer experience, reducing the time consumed for security processes. 

Given these valuable benefits of face recognition technology, it’s predicted that 97% of airports will roll out this technology by 2023. But this is not the end of the story, because the airports are lacking in using this technology throughout the passenger flow in the airports. Maybe because they lack resources that Hartfield-Jackson Airport holds. This airport in Atlanta uses a multi-faceted facial recognition system that scans the passengers to verify their identities at various check-points in the airport. This airport provides an ultra-modern view of what the future holds for the travel industry. 

Businesses utilizing facial recognition

Businesses are always in a bid to deliver the best to their customers, especially the ones that operate in totally private sectors, with no government players. Industries such as e-commerce, Fintech, Regtech, identity verification, and blockchain are trending as they’re carving the future of how businesses will be conducted in the future. 

These industries are the primary users of next-generation technologies due to nature of their products and services. The identity verification industry uses it in customer due diligence and identity screening solutions. These solutions use face verification along with document screening to ensure fool-proof security and seamless consumer onboarding. Fintech and blockchain ventures are using the solutions of the identity verification industry to onboard their remote customers without any false positives. 

Other than that, some tech giants such as Google, Facebook and Amazon are also using in-house face recognition systems. Mobile phone producers are also using face recognition to give a better experience to their consumers. All these unconventional use-cases of this technology have made it a household term. 

Face recognition for crime control

Crime control authorities are always in a bid to bridge the loopholes in social infrastructure which may lead to criminal activities. FBI uses facial recognition to identify the suspects, and if a match is found in the database of criminals, it can be used as the proof l to pursue a lawsuit against him. 

Also, the police department in some states of the U.S uses face recognition technology to identify the criminals in the recordings of public cameras. The police departments are still working on this initiative as the social activists have found privacy related loopholes in it. 

What are the hurdles in the growth of face recognition technology

Regulatory authorities have not given a free hand to the entities in using this technology. There are some restrictions on the use of public data. 

The Anti-surveillance ordinance signed by San Francisco’s Board of Supervisors banned the city agencies to use face recognition technology in 2019. Other states that ban the use of surveillance camera recordings to find the suspect are Somerville, Oakland, and San Diego. 

The best solution to overcome these hurdles is to use the technology with care and to handle the customer data with care. 

Customer data and privacy is the primary concern of the regulatory authorities hence the reason the laws such as GDPR and CCPA are introduced to control the use of customer data by the private sector. 

So far the private sector is the one that is least affected by the hurdles in the use of facial biometrics, compliance with data protection regulations and cautious usage of customer data will lead to the projected growth of the facial recognition industry. 

To wrap up, businesses from every corner of the world are onboarding remote customers and initiating business relations with global business entities. Face recognition solution enables them to onboard customers and to allow secure login to them in the future. As the businesses have a bigger and clear field to play, fully utilizing the potential of this technology controlled with data protection practices will lead to retainable growth. As for the public sector and government agencies, the future is bright one they have developed reliable in-house solutions.

European parliament imposes ban on facial recognition technology

European parliament imposes ban on facial recognition technology

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Following a public outcry after the leak of an internal memorandum discussing the utilization of facial recognition technology, the European legislative assembly decided to place a ban over its use. The European parliament’s intranet posted on one of its pages that facial recognition and artificial intelligence assisted translation would affect working techniques, processes, staff profiles and the contraction of services. 

A notable member of the European parliament and staff unions raised questions on the potential use of facial recognition which led to the removal of the parliament’s ‘digital transformation program.’ A parliamentary spokesperson stated, “There is no project of facial recognition in the European parliament,” adding that it was “not foreseen at any level.” This incident has led to an embarrassing situation for the parliament as a temporary ban on facial recognition was expected from the EU executive earlier. 

The facial recognition technology is expected to be banned in all public places such as stations, stadiums, and shopping malls. The ban will be announced by the European Commission by the end of the month, lasting for up to three to five years. The Dutch Liberal Member of the European Parliament Sophia in ’t Veld, who sits on the European parliament’s civil liberties committee, sought information on benefits, costs, consequences and coherence with EU’s data protection regulation. An internal staff union initiated a complaint in an email sent to all 705 members of the European Parliament and many of the parliament’s 7,500 officials.

The page was removed shortly after the complaints emerged. The EU spokesperson explained, “One exploratory project of the EP administration is to study and understand the potentials and threats of AI applied to parliamentary and administrative activities of the institution,” He further added that data protection was and will forever remain a clear priority for the European parliament and its administration.

The Members of the European Parliament will meet up next week for Committee Meetings. Many interesting votes and debates are scheduled to take place in favor of and against the ban on facial recognition technology.

Deepfakes on TikTok raise new security concerns

Deepfakes on TikTok raise new security concerns

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Deepfakes have started showing up on the popular video app TikTok, owned by ByteDance, a Chinese internet company. It has become one of the most popular social media platforms among the youth and some users are even becoming famous through the application’s wide reach. 

Deepfakes are manipulated videos or other digital representations produced by sophisticated AI technology, that yield false or counterfeit pictures and sounds that seem to be real. It involves the use of powerful techniques such as Artificial intelligence and Machine learning to create visual and audio content that has a high potential to deceive.

Charli D’Amelio, a fifteen-year-old, has due to her amazing choreography and dancing skills, become one of the most popular influencers on TikTok. Some TikTok users are taking up her videos and exploiting her content to attain popularity. This is done through deepfakes. 

For instance, Jesse Richards (@deepfaker), a TikTok user with 7,500 followers, has reposted some of Charli’s videos on his own account. In some of the videos, he has replaced her face with the face of The Office character Michael Scott, for viral purposes. 

Deepfakes are generally considered unethical and are disliked in many communities because they can give a perception that someone said or did something they have actually never done.

Deepfakes technology has raised concerns over how it could be used to spread false information and damage reputations of anyone anywhere. During the Trump-Hillary political conflict, many deepfakes were created for entertainment purposes. Because of the potential drawbacks, Facebook has announced that it would completely remove deepfakes from its platform. 

Furthermore, Twitter also reveals its plans for addressing deepfakes. 

Facial recognition technology grooving into cruise ships for ID verification

Facial recognition technology grooving into cruise ships for ID verification

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No more boarding frustration, no more standing in the queues for hours…

The travel industry is revamping the ways of customer boarding by streamlining the user experience at airports and now at cruise terminals. The overhead of time-consuming duties, holding travel documents along and their verification at each terminal used to thwart the travel experience of customers. To counter such issues, the overriding goal is to eliminate friction from the overall passenger experience.

For this, the travel industry considered the issues among which the most correspond to boarding time at airports that take hours and ultimately affect the tourist experience. To pander to such boarding issues, the travel industry is handshaking with technology experts to streamline their boarding operations. For this, the federal government is consulted to allow innovative solutions to deploy them at airports and cruise terminals.

Airports across the world have already installed AI technology-based systems which are now expanded to the cruise ships. A survey shows that facial recognition technology will be used in the top 20 airports of the U.S. for 100% international passengers in which American citizens are also included.

To streamline the passenger boarding experience, facial recognition systems are used for customer identification. Based on AI algorithms and techniques, facial recognition helps authenticate passengers.  

Travel Services that can be Streamlined with Online ID verification

Cruise ship travel identity verification solutions based on technologies help the industry rationalize the methods of identity verification at the time of passenger boarding. The following are some other use-cases in which technology enables the industry to smoothen the operation and authorizing users hence, ensuring both security and customer experience simultaneously. 

Travel Booking

For online booking at the cruise ship and other travel mediums, effortless checkout can be ensured by integrating the system with robust identity verification services. Registration can be done at the time of account creation where facial recognition technology helps streamline the customer experience.

Online Hotel Check-in

Using online credit card services and ID scanning facilities, manual identity verification can be eliminated and hotel bookings can be done with minimal effort.

Cruise Terminal Check-in

Security checkpoints at cruise terminals can be secured by providing passengers a facility of proving their identity quickly through a facial recognition system. Hence international travel can be made frictionless for customers and security can be ensured efficiently.

Credit Card Verification

The travel industry now does not need to suffer credit card fraud, chargebacks, and online payment scams. By deploying an AI-based facial recognition system, cruise ships can deter the risks of cybercriminals by verifying the credit card before the checkout process. 

Cruise Ship Passengers ID Verification

Identity verification is a crucial requirement and is achieved in many cruise ship operators such as Royal Caribbean Cruises by integrating facial recognition technology. Taking advantage of technological advancements, AI has solved the problem of identity verification in cruise ships. Jay Schneider, Royal Caribbean’s SVP of digital says that   

“Traditionally it would take 60 to 90 minutes to go through the process of boarding a ship, and as a result, people didn’t feel like they were on vacation until day two – we wanted to give them their first day back.”

After identity verification of passenger through facial recognition technology, cruise ship, just like airlines, has minimized the passenger boarding time. Royal Caribbean estimated customer boarding time to be 10 minutes. The time-consuming task of identity verification of customers that used to take hours can now be done in minutes. 

To achieve this, facial recognition technology played a great role and by using its computer-vision underlying technologies, travelers can be recognized and board by eliminating the need for manually identifying the travelers on a cruise ship. 

Facial Recognition Technology – Ensuring customer experience and online security

The global digitization, on one hand, is promoting the use of technologies wile streamlining business operations and on the other hand, it is giving rise to various challenges that accelerate most of the times due to bad actors in the system. To ensure online security in the system without compromising the customer experience, facial recognition technology fits in better. 

Facial recognition technology helps the travel industry identify each onboarding customer by allowing biometric sign-in. The online accounts are created which allow the user to perform the aforementioned activities and get online services. From ticket booking to boarding. ID verification in a cruise ship that used to be a time-consuming process can now be performed in mere seconds due to a facial recognition system.

Moreover, the transaction abandonment rate can be reduced by integrated just one API that allows the customer to identify themselves for the security aspect and in return get a seamless experience that would ultimately minimize customer abandonment are at the checkout time. 

Facial recognition system harnesses Artificial intelligence algorithms and methods to identify the unique facial features of onboarding customers, secure them in the database and verify at the time of verification. Just a matter of seconds keeps customer experience intact and reduces the risks of fraudsters from entering into a legitimate system. Hence deter the rate of malicious activities and makes the travel industry more secure and sound. 

Facial recognition market to grow 12.5% by year 2024 i.e. $9.06 billion, which worth $4.51 billion back in 2018.

Facial recognition technology is gaining prominence in various industries. From education to travel and secure online payments to the travel industry, all opt to take advantage of this technology to the full across the world. Facial recognition technology is capable of fighting against bad actors and unauthorized access. Therefore combating online frauds, payment scams, and other criminal activities from the travel industry.

EU’s decision to ban Facial Recognition for five years divides the tech giants

EU’s decision to ban Facial Recognition for five years divides the tech giants

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Facial recognition is emerging as a conflicting technology causing key disagreement among the world’s giant tech companies. Google CEO, Sundar Pichai is somehow in favor of the temporary ban on facial recognition technology as suggested by the EU, but Microsoft’s chief legal officer Brad Smith is not.

Recently, as per draft document obtained by POLITICO, European Union leaders suggested the ban on the use of facial recognition in public places for up to five years. It may be extended until the safeguards are in place to mitigate the technology risks.

This decision of the EU has caused a rift between many big technology companies. Earlier this week on Monday, Pichai didn’t show any dissatisfaction with the decision of ban on the technology; in fact, he said in a conference

 “I think it is important that governments and regulations tackle it sooner rather than later and give a framework for it. It can be immediate but maybe there’s a waiting period before we really think about how it’s being used … It’s up to governments to chart the course.”

However, Smith had some other thoughts regarding this decision. In his interview published last week, he was dismissive of the idea. According to him, facial recognition is a “young technology and it will get better with time” and when you can solve the problem that enables good things to get done and stop bad things to happens then let it be.

He said in his interview

“But the only way to make it better is actually to continue developing it. And the only way to continue developing it actually is to have more people using it.”

Law enforcement and private enterprises from all over the world are actively using facial recognition technology to identify people in public spaces. While some people are in favor of the technology that helps solve crimes, critics argue that it’s unsupervised and unchecked technology that poses a threat to civil liberties due to algorithmic bias.

The two executives’ comments come as a result of an unofficial statement of the EU five-year ban on the use of facial recognition in public spaces. However, there could be changes in the EU’s statement when announced officially. As per the leaked information, this temporary ban would give governments and regulators time to assess the dangers and risks associated with the technology.

Facial Recognition Technology Pioneered at Olympic and Paralympic Games Tokyo 2020

Facial Recognition Technology Pioneered at Olympic and Paralympic Games Tokyo 2020

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Facial recognition has grown by leaps and bounds with the arrival of the sophisticated pattern-matching abilities of modern artificial intelligence technology called neural networks. On one hand, leading cities like  Massachusetts, and San Francisco, California are to bar police from using pervasive computer surveillance technology, on the other hand facial recognition technology is going to used for Tokyo Olympics 2020. It is the first time the Olympics is using facial recognition technology.  It won’t be a wholesale replacement for the old ways: Accredited personnel at the Olympics will still have to wear traditional ID lanyards, Intel and NEC said. But the facial recognition system will be required: if someone loses their lanyard or tries to get access with one that’s stolen, the facial recognition system will block them.

Who is going to provide the system?

NEC Corporation, a Tokyo 2020 Gold Partner, will be providing the face recognition system at olympics. The facial images of every accredited person will be collected beforehand and stored in a database; these will be used to verify identities at accreditation checkpoints. Facial recognition system from Japanese electronics giant NEC and chipmaker Intel will be used to get where athletes, sponsors, journalists or volunteers at the 2020 Olympics in Tokyo need to be.

“Facial recognition improves security and efficiency by being able to confirm a picture ID against the face of the person seeking to enter a facility with greater speed and accuracy than human staff,” NEC said.

Intel with core i5 processor and the company has announced to provide a range of technologies as an official partner of the global event. NeoFace will be deployed for physical access control at venues event staff, athletes, media and volunteers. 

The Tokyo Organizing Committee of the Olympic Games (Tokyo 2020) has partnered with International Olympic Committee and other partners, Intel will also provide a computer vision solution for 3D Athlete Tracking, VR training for venue managers, and processing for the Cisco-powered digital networks.

Intel provides a VR experience and hosts an esports tournament prior to the Olympic Games. It also provides a call and response beat for the audience to clap and respond beat for the audience to clap along using their artificial intelligence capabilities. 

Intel & Olympic Related Events

Intel will be involved in other Olympic-related moves, too:

3D Athlete Tracking (3DAT)

Intel develops a technology called 3DAT (3D Athlete Tracking) that uses data about player movements broadcasters can use to boost instant-replay videos. An AI system processes video data to generate the overlay graphics.  

Global Esports Gaming Competition

In parallel with the Olympics in Tokyo Intel also is helping to run a global esports gaming competition. It also includes participation from gaming companies Capcom and Epic Games where Players from an initial group of 20 countries will compete in the videogame event. 

Virtual Reality Training

Athletes and organizers can use to visualize arenas and other facilities by virtual reality training realms built by Intel.

Purpose of Facial Recognition Technology in Tokyo Olympics

Following are the reasons how facial recognition technology will be enhancing the security of the Tokyo Olympics:

Physical Access Control

Physical access control is the process to control who, where, and when to provide access. It determines who is allowed to enter or exit the system, where a certain person is allowed to enter or exit, and when they are allowed to enter exit or enter. With FRT these only recognized persons are given access. The system will match the faces in the database and provide access when a face verified by the system. 

Monitor Audience

This technology will be used to monitor audience activity throughout the matches to analyze if any abnormal activity is going on. This technology will help to provide assistance to anyone in the audience who will be seeking it. It will be a quick analysis of millions of spectators for many reasons. It can be used to count the number of advance present and regular fans can be monitored as well. This technology has previously been used in Taylor Swift’s concert to highlight her fans. 

Look out for Suspicious Entities

During audience analysis, suspicious individuals can be caught on camera. If anyone seems involved in any kind of suspicious activity he can be continuously monitored through cameras. This enhances the security system and provides speedy solutions. NEC will use hundreds of facial recognition systems around the Olympics facilities to speed up ID checks for accredited people.

Improve Comfort & Convenience

The 2020 Olympics organizers say the facial recognition is twice as fast as regular ID checks, meaning shorter waits in line, and that it’ll improve security and that too by preventing spoofing and unauthorized access into important areas in the venue. 

By using facial recognition technology security system becomes seamless and wok flow is smooth. It is just the face that is to be shown to get access to the ground. People will not have to wait in long ques to get access granted. This will improve the efficiency of the security system and provide convenience to the people. 

Facial Recognition Technology and Personal Data Concerns

The personal information storage of individuals will comply with the Personal  Information Protection Law and it cannot be said how long will the data be kept in the system. All personal data is managed appropriately during the games and will be securely deleted afterward that too under strict conditions. The photos of each accredited person are collected with their consent at the time of issuing the accreditation card somewhat similar to the accreditation process in the past games. 

Facial recognition technology will not be a replacement for old methods but will be used along with them. The accredited persons will have to wear the traditional ID cards. But FRT will be required to in case someone loses their card or tries to get access with a stolen ID card as FRT will block them.

“Facial recognition improves security and efficiency by being able to confirm a picture ID against the face of the person seeking to enter a facility with greater speed and accuracy than human staff,” NEC said.

So data stored will only be used for the purpose of security and will be discarded right after the event is over.

Facing the Future: FRT for FIFA World Cup 2022

According to the company CEO  of NtechLab, Alexander Minin, the company is in negotiation with FIFA and Qatar to supply its facial recognition technology for a pilot project at the upcoming World Cup of soccer. The company is also one of several reported to be in ongoing negotiations to broadly deploy facial recognition at large Russian airports.

The applications of facial recognition technology are growing great in number. We can not stop this tide but we can manage it by giving the related data in the right hands. This technology can provide enhanced security at the Olympic games with convenience and ease. The system has operated with a false rejection rate of under 2 percent providing more accuracy.

Top 8 Facial Recognition trends to watch in 2020

Top 8 Facial Recognition trends to watch in 2020

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Facial recognition has been gaining prominence in recent times, owing to the benefits it offers over traditional security methods. The facial recognition market was valued at 5.07 USD billion in 2019 and is expected to reach a value of 10.07 billion USD by 2025. Face recognition is one of the most common forms of biometric authentication. 

As we enter a new decade, the use of artificial intelligence-powered facial recognition technology is expected to become more commonplace. From day to day tasks such as unlocking smartphones to identity verification in digital banking, our day to day interactions with face authentication software will continue to grow.

Whether it’s helping leading retailers like Amazon to identify the customers or assisting the governments in the airport and border security management to identify potential threats, face verification technology is changing the game across a wide range of sectors. However, the next decade is expected to see a major boom in the face recognition industry. 

In this article, we will discover 8 face recognition trends that are set to shape the landscape in 2020 but before that, we will discuss what it is and how it works.

What is Face Recognition?

Facial Recognition is a technology, which is capable of identifying and verifying a person using their face. It captures, analyzes and compares patterns based on a person’s facial features.

Now let’s take a deep insight into how we can expect our interactions with face recognition technology to increase as we move into 2020.

Top face recognition trends

E-Commerce and Digital Banking

In the world of digital services, issues of identity and authentication pose am a threat. It’s not easy to trust someone when they are not physically present. Traditional knowledge-based methods have already been tested and clearly, they aren’t the solution to online protection. Verification methods like passwords and emails are vulnerable to hacking and difficult to memorize.

For foolproof security, something more robust is needed and one of the solutions that experts believe to take over these methods is biometrics using facial features. Needless to say, face recognition technology is becoming commonplace in the online industry and is expected to be implied in digital transactions for fraud mitigation.

Personalized Customer Experience

Something that we can expect more and more companies to embrace is personalized customer experience and biometrics can enhance this feature. With customers able to login using their face, they will access personalized content specifically designed for them based on their preferences. Online education is one of the major sectors to adopt personalized customer experience.

Airport Security and Border Security Management

Delta airlines have already implemented face authentication technology at Atlanta airport. However, the company has confirmed that by the end of 2020, more than 20 airports will have facial recognition to improve the customer journey. Customers will be able to use their faces to check-in, check their luggage, and board a plane. 

Even though concerns were raised on the use of facial recognition for identification at the airport, however, 73% of the customers said that they would feel comfortable using this technology after the single pass through Delta’s curb-to-gate facial recognition system.

Health Care

Given the health care is under constant pressure from both physical and digital security breaches, the healthcare sector is investing in facial recognition technology to enable more advanced security and fraud prevention. From preventing health insurance fraud to detecting diseases, biometric technology is already being used in the health sector. Expected facial recognition trends in the health care sector in 2020 includes:

  • Tracking patients’ medication history
  • Detecting genetic diseases
  • Preventing health insurance fraud

Cardless Payments

Chinese payment systems are already ahead of the rest of the world with secure systems like AliPay and WePay. Face authentication presents retailers with more valuable data and it has the potential to remove shopping cart barriers. Amazon and other big e-commerce companies are expected to roll out their version of facial recognition for payments in 2020.

Tourism and Travel

Forget locking yourself out of a hotel room or having to take your key card everywhere you go. While booking a room, either in person or over the phone, customers will be able to register through their faces. In hotels that use face verification technology, the check-in time is reduced by 40%. Facial recognition technology is expected to be seen everywhere from flight booking to checking in at the resort.

Smart Technologies

With Apple launching FaceID, smartphones have become a big customer area where facial recognition technology is being implemented. Face recognition technology is also becoming prevalent in other forms of smart technologies such as smart TV and smart home security system.

Digital Advertising

As retail stores look to personalize customer experience for customers, facial recognition is being used to identify individuals and their demographics to present promotions and content tailored to their demographics. This might be a little controversial but this trend is to be seen around 2020.

Facial recognition is already being used and integrated into daily aspects of our lives and the future of this technology is an exciting one. While there are concerns raised over the use of this technology, people are still adopting and embracing facial recognition technology for improving their experience.         

Uber joins lawsuits to exempt itself from California law

Uber joins lawsuits to exempt itself from California law

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California law effective from today, i.e. Jan 1 is all set to give equal protection to independent contractors.

Uber, the ride-sharing company and Postmates, on-demand meal delivery service, filed a lawsuit on 30 December in U.S. Court Los Angeles, to block the California law that was supposed to give protection and wage to the independent contractors. 

The lawsuit argued that the new California law violates the federal and state constitutional obligations of equal protection benefits and due process. Moreover, Uber said that it will try to link this lawsuit to the other challenge filed by the association (representing photographers and freelance writers) in mid-December.

In November 2019, the first challenge to the law was filed by California Trucking Association on behalf of independent truckers.

This new law is coming up with the nation’s strict rules according to which the independent workers must be considered as employees and this can set an example for other states. Lydia Olson, the ride-share drive wrote about his concerns in a Facebook post cited by Uber

“This has thrown my life and the lives of more than a hundred thousand drivers into uncertainty,” 

Lorena Gonzalez – democratic assemblywoman of San Diego raised her concern that more than one million California workers lack primary benefits including minimum wage, paid sick leaves, medical expense coverage, mileage reimbursements, etc. and therefore, the employee rights must be extended.

When the lawmakers were trying to craft the law, uber has tried to exempt itself from the obliged entities claiming it would defend its labor model from legal challenges. Moreover, uber joined DoorDash and Lyft in a vow to spend $30 million each to upturn the law if they don’t win the case in 2020.

Gonzalez said in one of her statement

“The one clear thing we know about Uber is they will do anything to try to exempt themselves from state regulations that make us all safer and their driver employees self-sufficient. In the meantime, Uber chief executives will continue to become billionaires while too many of their drivers are forced to sleep in their cars.”

The lawsuit by Uber contends that the law exempts some industries and it meddles the worker’s right to choose how they make living hence, they can void their existing contracts.


Facial Recognition Market to Grow 12.5% Annually Through 2024

Facial Recognition Market to Grow 12.5% Annually Through 2024

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According to a new report by Mordor Intelligence, the facial recognition market is expected to grow at an annual growth rate of 12.5% for the forecast period 2019 to 2024. In 2018, the market was valued at $4.51 billion and by the end of 2024, it is expected to reach a value of $9.06 billion by 2024, according to the press release. 

Facial recognition is quickly picking the pace and will be surpassing fingerprint scans in the future. At present, around 94% of smartphones feature fingerprint sensors but this is expected to drop to 90% by 2023. 

Facial recognition

(Image Courtesy: Mordor Intelligence)

The increase in the 3D cameras market is also expected to bring advancements and new applications for 3D facial recognition technology. The areas of healthcare, commerce, payments, and IT solutions are benefitting a lot. 

Facial recognition systems are also being adopted for widespread mass surveillance to enhance safety and security. This is another reason for the increased market for facial recognition. Government-led initiatives are also contributing to the double-digit growth of such technologies.  

North America is expected to hold the highest market share for facial recognition technology as it offers huge opportunities for homeland security and criminal investigations. The biggest facial recognition system is being operated in North America by the FBI. The ID system of FBI maintains a database with data on more 117 million Americans and conducts an average of 4055 searches every month to identify individuals. In 2017, the US alone witnessed 1579 data breaches and 8% of the data breaches were reported by financial institutions.  Due to these factors, facial recognition technology to provide a more enhanced layer of security is imperative. 

New Study by NIST Reveals Biases in Facial Recognition Technology

New Study by NIST Reveals Biases in Facial Recognition Technology

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The National Institute of Standards and Technology (NIST) recently did a study on the effects of race, age and sex on the facial recognition software. The results showed significant biases in the software for the people of color and women. 

The report’s primary author and a NIST computer scientist, Patrick Grother, said in a statement, 

“While it is usually incorrect to make statements across algorithms, we found empirical evidence for the existence of demographic differentials in the majority of the face recognition algorithms we studied.”  

This means that the algorithms misidentified people of color more than white people and they also misidentified women more than men. 

The study done by NIST is pretty robust. NIST evaluated 189 software algorithms from 99 developers, “a majority of the industry”, using federal government data sets which contains roughly 18 million images of more than 8 million people.

The study evaluated how well those algorithms perform in both one-to-one matches and one-to-many matches. Some algorithms were more accurate than others, and NIST carefully notes that “different algorithms perform differently.” 

The higher rates of false positives were in the one-to-one matching scenario for Asian and African American faces compared to Caucasian faces. And this effect was remarkably dramatic as well in some instances with the misidentifications 100 times more for the Asian and African American faces compared to their white counterparts. 

The study also showed that the algorithms resulted in more false positives for women than men and more false positives for the very young and very old compared to the middle-aged faces. In the one-to-many scenario, African American women had higher rates of false positives. 

The study also found out that the algorithms developed in Asian countries didn’t result in the same drastic false-positive results in the one-to-one matching of Asian and Caucasian faces. According to NIST, this shows that the impact of the diversity of training data, or lack thereof, on the resulting algorithm. 

“These results are an encouraging sign that more diverse training data may produce more equitable outcomes, should it be possible for developers to use such data,” Grother said.



How Deepfakes Deceptions are Affecting Businesses

How Deepfakes Deceptions are Affecting Businesses

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The word deep fake comes from the terms “deep learning” and “fake,” and is a type of artificial intelligence. In simplistic terms, deep fakes are falsified videos made by means of deep learning. Deep learning is “a subset of AI,” and refers to arrangements of algorithms that can learn and make intelligent decisions on their own. This technology can be used to make people believe the false. According to Peter Singer, cybersecurity and defense-focused strategist and senior fellow at New America think tank the danger of this technology is that it can be used to make you believe something is real when it is not.  These deep fakes are a new kind of video featuring realistic face-swaps. In short, a computer program finds common ground between two faces and stitches one over the other. If source footage has high resemblance the transformation is nearly seamless. 

Deepfakes are so-named because they use deep learning technology, a branch of machine learning that applies neural net simulation to massive data sets, to create a fake. The source face is transposed onto a target like a mask by using artificial intelligence. 

Why Deep Fakes are being used?

Deep fakes have various purposes to be used. Following are the details:

Original Purpose:

 Deep fakes were developed to be used to make mimicry of someone from a funny perspective. This technology has been used in many 3D movies for famous characters. Deep fakes were utilized to leak funny videos. This technology was used by the film industry for many purposes. 

Technology Turning Bad:

Now, this technology is being widely used to deceive people by cybercriminals. Nowadays, there are several different ways to swap faces in a very realistic way. Not all use AI, but some do: deep fake is one of them. Such technology can be used to fool a system and gain access. This poses a major threat to businesses across the globe. New technologies are being used to deceive people online.  A recent focus on disinformation and fake news has sparked concerns among the public. In the past, only an expert forger could create realistic fake media by using deceptive techniques. But now using machine-learning allows anyone with a smartphone to generate high-quality fake videos. Such videos can incite panic and sow distrust in businesses to produce other harmful outcomes. Because of potential harms businesses have expressed concerns about deep-fake technology. Deep-fake technology can create such realistic-looking content that represents an unprecedented development in the ecosystem of disinformation. The content produced by deep fakes seems so real that the viewers are induced to trust it and share it on social networks thus hastening the spread of disinformation. 

Ways Deep Fake Deception is a Threat to Business:

Following are the threats that deep fakes pose on businesses: 

Tarnish Business Reputation:

Deepfakes are to spread fake news against anyone. Videos of CEOs are made previously which has been used against the businesses. Such cases spoil the company’s reputation catastrophically. KYC is a mandatory thing to be performed by business in order to verify their customers and KYB to authenticate other businesses. Such deepfake videos can be used to trick the system and hence causing the unauthenticated and wrong people to be taken on board. Such customers are the biggest threat to businesses as they will use your business to run illegal activities like money laundering, terrorist financing, cybercriminal activities like data breaches. Ultimately it’s the business whose name and reputation will be spoiled in all these scenarios. 

Social Engineering:

Social engineering and fraud are by no means a new threat to businesses, with spam, phishing, and malware routinely targeting employees and businesses’ IT infrastructure. Most corporate entities have adapted to deal with these threats, employing robust cybersecurity measures and educating employees.

However, deepfakes will provide an unprecedented means of impersonating individuals, contributing to fraud that will target individuals in traditionally ‘secure’ contexts, such as phone calls and video conferences. This could see the creation of highly realistic synthetic voice audio of a CEO requesting the transfer of certain assets, or the synthetic impersonation of a client over Skype, asking for sensitive details on a project.

These uses of deepfakes may seem far-fetched, but as an example by a Buzzfeed journalist demonstrated last year, even primitive synthetic voice audio was able to convince his mother he was speaking to her on the phone. The threat here is derived from an existing assumption that this kind of synthetic impersonation is not possible.

Previous examples of direct audio-visual impersonation scams read like something out of a Hollywood film. One recent case involved Israeli conmen stealing €8m from a businessman by impersonating the French foreign minister over Skype, recreating a fake version of his office, and hiring a makeup expert to disguise them as the minister and his chief of staff.

Biometric security measures such as the voice and facial recognition used in automated KYC procedures for onboarding bank customers may be compromised by deepfakes that can almost perfectly replicate these features of an individual.

Extortion against Influential Business Leaders:

If not in an attempt to manipulate markets, deepfakes will also enhance and likely increase extortion attempts against influential business leaders. 

Fake videos or audio of business leaders could be generated quickly using deepfakes which ends leveraging existing damaging rumors or fabricating new scenarios. These videos can be used to blackmail by saying that these are real for ransom or posing the identical threat of significant damage to the individual’s reputation.

The authenticity of the defamatory video or photos will become irrelevant in this deepfake extortion. Such videos have the potential to cause catastrophic damage to individual and corporate reputation.

Market Stock Manipulation:

Such deepfakes also have significant potential to enhance market manipulation attacks in addition to scams and direct impersonation. This could involve the precise and targeted publication of a deepfake, such as a video of US President Donald Trump promising to impose or lift tariffs on steel imports, which cause a company’s stock price to plummet.

Another good example of how deepfake market manipulation could play out can be seen with the recent erratic behavior of Paypal co-founder and Tesla CEO Elon Musk. 

The public expectation of such volatile behavior from Musk makes him a prime target for deepfakes that depict him acting in a damaging way, further impacting Tesla’s share price and corporate reputation. However, the time required to confidently prove a video or photo is a deepfake may make such rollbacks impossible.

How Can you Protect your Business?

Following are some precautionary measures that every business need to adopt to be on the safe side by deep fakes:

Train Your Employee: Give proper training to your employees to detect real and fake images and videos before using them.

Monitor your business Online: Always check for your business-related videos online to filter out if there is any fake image or clip is present related to your company or representatives.

Incorporate the latest technology: The latest technology should be used to fight back scams that come with the latest technologies. Always stay a step ahead by having a tech-powered system.

Be Transparent: Be transparent and have a filter system to hinder any such activity to save your business. 

It is essential that the corporate world prepares for the inevitable impact of deepfakes, educating employees about this emerging threat, and integrating media authentication tools into their data pipelines. Failing to do so may lead to irreparable damage to corporate reputation, profits, and market valuation. So deep fakes can cause real damage to businesses if the right steps are not taken on time. 

Homeland Security Takes Back Its Plans of Facial Recognition for US Citizens

Homeland Security Takes Back Its Plans of Facial Recognition for US Citizens

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The Department of Homeland Security (DHS) is taking back its decision to implement a policy that would require all US citizens to undergo facial recognition scans while entering or leaving the US. The policy was introduced last week and required US citizens to have their faces scanned and added to a biometric database. 

Now, the US citizens will not be required to participate in facial recognition scans at airports with DHS retracting the policy. Customers and Border Protection (CBP) said on Wednesday that the reversal in policy was the result of conversations with ‘privacy experts’, lawmakers and travel-industry stakeholders. 

John Wagner, a Border Patrol said in a statement, “CBP is committed to keeping the public informed about our use of facial comparison technology. We are implementing a biometric entry-exit system that protects the privacy of all travelers while making travel more secure and convenient.”

Non-US citizens are already required to undergo facial recognition scans when entering the United States. When it was announced last week that CBP would require US citizens to go through facial recognition scans as well, the proposed rule was met with backlash from privacy and human rights advocates. 

American Civil Liberties Union analyst, Jay Stanley, said in a statement, 

“This proposal never should have been issued, and it is positive that the government is withdrawing it after growing opposition from the public and lawmakers.” 

The full statement regarding the rule reversal can be read here. 

Homeland Security Wants Facial Recognition For All Entering or Leaving US

Homeland Security Wants Facial Recognition For All Entering or Leaving US

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The federal government is considering changing airport security in a major way. Facial recognition technology is being used everywhere from our iPhones to CCTV cameras in the streets. The technology is also being used for years for non-US citizens arriving in the states but it has not been a requirement for the US citizens up til now. 

But now the Homeland Security wants to expand the use of facial recognition technology for anyone entering and leaving the US. In a recent filing, the DHS proposed amending existing regulations “to provide that all travelers, including US citizens, may be required to be photographed upon entry and/or departure” from the United States, such as at airports.

Director of entry/exit policy and planning at the Department of Homeland Security, Michael Hardin, told CNN Business that for now, the rule is in the ‘final stages of clearance’. But since it hasn’t been cleared yet, the rule won’t go into effect until after a period of public comment. 

Facial recognition technology has become ubiquitous in recent years with technology becoming remarkably common in airports throughout the world. DHS has to roll out facial recognition technology to the 20 largest airports of the US by 2021. A spokesperson for Customs and Border Protection said the agency ‘will ensure that the public has the opportunity to comment prior to the implementation of any regulation and the agency was ‘committed to its privacy obligations.’ 

China Makes Facial Recognition Mandatory For Smartphone Users

China Makes Facial Recognition Mandatory For Smartphone Users

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China is making it mandatory for all smartphone users who register new SIM cards to submit to facial recognition scans. The new rule went into effect on Sunday across the entire country. 

The guidelines first announced in September require telecom companies to deploy ‘artificial intelligence and other technical methods’ in order to verify the identities of people registering SIM cards. Physical stores across the entire country had time until December 1 to begin implementing the new rules.  

The Ministry of Industry and Information described the measure as a way to ‘protect the legitimate rights and interest of citizens in cyberspace’. Through mandatory requirements, Chinese mobile phone and internet users are extremely easier for the government to track. 

Already mobile phone users are obligated to register SIM cards through their identity cards or passports. Since last year, many telecoms had already begun scanning the customers’ faces. A number of social media platforms in China also require users to sign up with their ‘real identities’ through their phone numbers.

The increasing use of facial recognition in China has raised a lot of privacy concerns about information security and consent. Facial recognition is being used from middle schools to concert venues and public transport. 

Last month, the country’s first lawsuit was filed by a professor against the use of facial recognition. Guo Bing, a professor at Zhejiang Sci-Tech University claimed that a safari park in Hangzhou violated the country’s consumer rights protection law by scanning his face and taking his personal data without his consent. 

In September, China’s Education Ministry announced that it would ‘curb and regulate’ the use of facial recognition after parents became angry at the facial recognition software installed without their consent at a university in Nanjing to monitor the attendance of students and focus during class. 

Big giant tech companies in China are writing standards for the UN regarding facial recognition and video monitoring. Human rights advocates considered the measure as another step towards ‘dystopian surveillance state’. 

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