Facial Recognition Technology With in Increasing Verticals

Introduction

According to Facial Recognition Statistics, Facial recognition technology is an advanced system employing algorithms and AI to authenticate and validate individuals by analyzing distinct facial attributes. These attributes include eye spacing, nose shape, and jawline contours. This analysis generates a facial profile matched against databases or images to establish identity.

The technology’s versatility and impact on diverse fields have garnered substantial interest. For example, facial recognition is commonly used for security purposes, such as access control to buildings, airports, and restricted areas. It can help identify and track individuals on watchlists, alert authorities to potential threats, and aid investigations.

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• The facial recognition technology market stood at 5 billion USD in 2022 and is expected to grow to 19 billion USD in 2032 at a CAGR of 14%.
• The accuracy of facial recognition technology varies widely depending on conditions and use cases. In ideal scenarios, some algorithms achieve accuracy rates above 99%, while real-world deployments often have lower accuracy due to environmental factors and diversity in subjects’ appearances.
• Approximately 70% of governments globally employ facial recognition technology (FRT) on a significant scale.
• The likelihood of a random individual successfully unlocking your iPhone using Face ID is approximately 1 in a million.
• Facebook’s DeepFace can determine whether two depicted faces correspond to the same individual, with an accuracy rate of about 97%.
• By the conclusion of 2023, an estimated 97% of United States airports are anticipated to integrate FRT.
• The initial instance of an arrest assisted by facial recognition technology occurred at Washington Dulles International Airport in August 2018.

Global Facial Recognition Market

• The revenue generated from facial recognition technology exhibits a steady upward trajectory at a CAGR of 14% over the projected years. In 2022, it stood at 5 billion USD, marking the initiation of its growth.
• Subsequent years witnessed a consistent increase, with revenue reaching 6 billion USD in 2023 and further climbing to 7 billion USD in 2024.
• The growth trajectory remains robust, culminating in projected revenues of 19 billion USD in 2032. This evolving financial landscape underscores facial recognition technology’s increasing integration and significance across various sectors and industries.

Key Players in the Facial Recognition Industry

• The market for facial recognition technology is characterized by a distribution of market shares among several key players. Among them, Ayonix Corporation leads with a share of 15%, closely followed by IBM Corporation and Aware, Inc. with 11% and 10%, respectively.
• Other significant contributors include FacePhi and Gemalto NV, each holding a 10% and 9% market share, respectively.
• Additionally, Cognitec Systems GmbH, 3M Company, and Fujitsu command a collective share of 26%, divided amongst them with 9%, 9%, and 8%, respectively.

Country-wise Use of Facial Recognition

China

• China ranks 5 out of 40 on the list, which is hardly unexpected given its reputation as a significant facial recognition technology provider. The government and law enforcement agencies extensively employ this technology, sometimes resorting to intrusive surveillance methods.
• An illustrative case involves Suzhou, which used technology to publicly humiliate seven individuals for venturing outside in their pajamas.
• The city subsequently shared its images on its WeChat account by employing Facial Recognition Technology (FRT) to identify these individuals. Another instance highlights a Chinese park’s utilization of FRT to deter the pilfering of toilet paper.

Russia

• Ranked at number 9 out of 40, Russia’s prominent position on the list is hardly unexpected.
• Facial recognition technology features prominently across all the categories discussed, signifying that Russia, like other nations, is increasingly adopting facial recognition technology across diverse domains.

United Arab Emirates (UAE)

• Ranked at number 10 out of 40, the United Arab Emirates (UAE) is taking a page from other top-ranking countries by implementing facial recognition technology extensively.
• This deployment is aimed at expediting processes and reducing fraudulent activities.
• The technology’s applications are diverse, from accessing government services to recording school attendance, making its integration pervasive throughout the UAE.

Japan

• Japan has a score of 12 out of 40. Facial recognition technology (FRT) is employed across all the categories examined in Japan. A noteworthy area of focus is the utilization of FRT in conjunction with citizens’ social media profiles to locate individuals involved in criminal activities. This practice raises substantial concerns.
• The National Public Safety Commission (NPC), responsible for maintaining facial images of approximately 10 million Japanese citizens, has granted the police access to this repository. Consequently, law enforcement can apply this database in conjunction with FRT for their operations.

India

• Ranked 12 out of 40, India showcases the implementation of approximately 16 distinct Facial Recognition Technology (FRT) systems across Central and State governments.
• Moreover, an additional 17 systems are currently in the process of being developed.

Australia

• Australia occupies the 13th spot out of 40 countries.
• Australian law enforcement was found to be employing the contentious facial recognition technology provided by Clearview, a company that garnered attention for building its database from social media images. Following this revelation, Victoria police opted to distance themselves from this technology.

United States

• The United States holds the 18th position out of 40 countries. While the adoption of this technology is expanding within the U.S., there is currently no indication of Facial Recognition Technology (FRT) being implemented in buses.

Breakdown of Industries Using Facial Recognition

Business

• Public attitudes toward facial recognition technology vary across different United States scenarios. For instance, approximately 30% of American adults find it acceptable for companies to utilize recognition for monitoring employee attendance.
• A separate study underscores that a mere 32% of consumers feel at ease with private companies employing recognition.
• In the context of advertising, contrasting opinions emerge. About 54% of adults in the U.S. do not support using recognition technology to assess consumer reactions to advertisement displays.
• Conversely, 30% of respondents regard this trend as acceptable. Regarding security enhancements in apartment complexes, perspectives are divided. Around 30% of U.S. adults support the integration of facial recognition by apartment owners to bolster security measures, while 34% express reservations.

Crime and Law Enforcement

• As of July 2021, the Government Accountability Office at the federal level disclosed that 42 federal agencies utilizing law enforcement personnel had incorporated facial recognition technology to varying extents.
• 59% of Americans deem it acceptable for law enforcement to utilize Recognition Technology (FRT) to evaluate security threats in public areas.
• Trust in law enforcement’s handling of FRT differs among racial groups, with 64% of White adult Americans, 47% of Black individuals, and 55% of Hispanic respondents agreeing.
• Interpol’s recognition system holds facial images from more than 179 nations, establishing a distinctive global criminal database.
• Since its launch in late 2016, Interpol’s facial recognition system has identified around 1.5K individuals of interest, criminals, terrorists, missing persons, or fugitives.
• In 2021, Maine pioneered enforcing stringent state-wide boundaries, permitting FRT solely for serious crime investigations.

Healthcare

• In June 2021, the Geisinger hospital network in northcentral Pennsylvania introduced facial recognition technology (FRT) across three facilities as part of its positive patient identification system. This FRT system uses facial biometrics to scan a patient’s facial features, storing and connecting the data to their electronic health record.
• This setup permits patients to securely confirm their identity by performing a one-time facial scan, expediting the appointment check-in process. The implementation of this system commenced in June, and it has already registered the facial profiles of more than 4,000 patients within the hospitals’ medical records.

Banking and Finance

• According to Aite-Novarica Group, approximately 15% to 20% of the 11,000 financial establishments in the United States employ a combination of selfie photo imaging and document verification to validate the usage of mobile or online banking and online application procedures.
• Prominent U.S. banks such as JP Morgan, Chase, City National Bank of Florida, and Wells Fargo have either experimented with or explored using artificial intelligence systems that incorporate facial recognition technology.
• Within the Latin American region, 42% of participants wanted future banking applications to incorporate the recognition feature.

Airports

• Regarding airport security, 54.3% of Americans support using facial recognition for safety screenings.
• Similarly, 54.8% concur that recognition should not face limitations if it enhances public safety.
• By 2023, facial recognition technology will be implemented in 97% of airports.
• The Customs and Border Control agency retains facial exit scans for up to 14 days post-identification.
• Since its adoption, the agency has applied recognition to over 2 million passengers across more than 15,000 flights.
• Their target is to extend this practice to passengers on 16,300 flights per week by the conclusion of 2021.

Accuracy of Facial Recognition Systems

• As of April 2020, the most accurate face identification algorithm exhibits an error rate of merely 0.08%, a significant improvement from the top algorithm in 2014, which displayed an error rate of 4.1%.
• Algorithms employed for verification, which compare individuals to well-defined reference images such as passport photos or mugshots, can achieve accuracy rates as impressive as 99.97% on established evaluations like NIST’s Facial Recognition Vendor Test (FRVT).
• In practical applications, actual accuracy rates tend to be significantly less impressive. For instance, the FRVT discovered that a prominent algorithm’s error rate increased from 0.1% when comparing against high-quality mugshots to 9.3% when comparing against images of individuals taken “in the wild,” where factors like the subject’s gaze not being directly at the camera or potential obstructions like objects or shadows could impact the results.
• NIST’s Face in Video Evaluation (FIVE) 2017 assessed that the most proficient algorithm demonstrated an accuracy rate of 94.4%.

Prominent Examples of Inaccuracy in Facial Recognition

• In 2018, the ACLU gained attention for uncovering that Amazon’s recognition technology mistakenly associated 28 members of Congress with individuals who had been arrested.
• In 2019, the Washington County Sheriff’s Office in Oregon, which employs Amazon’s facial recognition product, conveyed that they do not establish or employ confidence thresholds when utilizing the system. This instance underscores the significance of guaranteeing that operators handling recognition for critical purposes possess adequate training and supervision to ensure the correct configuration of these systems. 

Public Views of Facial Recognition Technology

• A recent survey in the U.K. aimed to gauge public sentiment concerning using Automated Facial Recognition (AFR) technology by various entities and in different settings, such as police, government, private sector, airports, public transport, schools, supermarkets, and workplace human resources departments. The survey revealed that 46% of respondents believed that the public should have the option to consent to or opt out of AFR use.
• Moreover, 55% agreed that the government should impose limitations on the police’s utilization of recognition technology.

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