Image Recognition Statistics


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Image Recognition Statistics 2023: Facts about Image Recognition outlines the context of what’s happening in the tech world.

LLCBuddy editorial team did hours of research, collected all important statistics on Image Recognition, and shared those on this page. Our editorial team proofread these to make the data as accurate as possible. We believe you don’t need to check any other resources on the web for the same. You should get everything here only 🙂

Are you planning to form an LLC? Maybe for educational purposes, business research, or personal curiosity, whatever the reason is – it’s always a good idea to gather more information about tech topics like this.

How much of an impact will Image Recognition Statistics have on your day-to-day? or the day-to-day of your LLC Business? How much does it matter directly or indirectly? You should get answers to all your questions here.

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Top Image Recognition Statistics 2023

☰ Use “CTRL+F” to quickly find statistics. There are total 47 Image Recognition Statistics on this page 🙂

Image Recognition “Latest” Statistics

  • According to Smith (2020), 56% of Americans believe that law enforcement will appropriately screen for dangers in public spaces using face recognition technology.[1]
  • Customer satisfaction will be improved, according to 62% of respondents, if face recognition technology is used to identify hotel visitors, according to Oracle Hospitality.[1]
  • In the United States, 86% of individuals are acquainted with face recognition technology, while 13% are completely ignorant about it.[1]
  • Only 32% of customers, according to a research by Capers (2020), are comfortable with private enterprises using face recognition technology.[1]
  • 59.4% of Americana people agree that if face recognition technology has a 100% accuracy rate, police should be permitted to use it to find offenders.[1]
  • 16.1% of Americans strongly agree that the use of surveillance cameras should be regulated by the government.[1]
  • 41% claimed that they are more inclined to stay at hotels equipped with automatic face recognition technology.[1]
  • According to Statista, 49% of people think that shops should use face recognition technology to prevent stealing.[1]
  • Gender identification is 99% accurate on photos of white men, but a facial recognition error rate of nearly 35% occurs when identifying the gender on photos of darker-skinned women.[1]
  • The face recognition market is anticipated to expand at a compound annual growth rate of 14.5% between 2020 and 2027. (Grand View Research, 2020).[1]
  • In the U.S, 30% of individuals agree that using face recognition technology to track employee attendance is appropriate.[1]
  • 74% of U.S respondents think face recognition technology works well for properly identifying people.[1]
  • In Spain, a study by CaixaBank shows that 70% of users would be willing to use facial recognition instead of PIN when withdrawing money from ATMs.[1]
  • According to Castro & McLaughlin (2019), 55% of respondents oppose strong regulation of face recognition, particularly when it is used to protect the public.[1]
  • Only 16% of Americans believe that the use of face recognition technology should be carefully limited by the government.[1]
  • According to Oracle Hospitality, 74% of hotel owners believe that by 2025, everyone will be using biometrics to identify hotel workers.[1]
  • By using face recognition technology in retail settings, violent events may be reduced by 91%.[1]
  • Regarding residential building owners using face recognition to improve security 34% of American people disagree, while 30% believe it is an appropriate measure.[1]
  • When it comes to ethnicity, White adult Americans (64%) agree that law enforcement can be trusted with facial recognition technology than Black (47%) and Hispanic (55%) respondents.[1]
  • The best performing face recognition system was able to achieve an identification rate of 96% despite the fact that system performance varied.[1]
  • About 30% of US citizens believe that using face recognition technology to track employee attendance is appropriate.[2]
  • 36% of americans believe that technology firms will appropriately utilize facial recognition.[2]
  • The majority of US adults—roughly 73% —believe face recognition can identify persons fairly accurately.[2]
  • In response to an ongoing terrorist incident, over 76% of Americans think law enforcement should employ face recognition technology to identify terrorists.[2]
  • 59% support the use of FRT by law enforcement to evaluate security hazards in public areas.[2]
  • 76% are in favor of mandating that law enforcement undergo training in FRT use and risk awareness.[2]
  • The majority of Americans—roughly 76% —support utilizing FRT in schools to detect known child predators.[2]
  • Facebook DeepFace has a 97% accuracy rate for determining if two faces in photographs belong to the same individual.[2]
  • Less than 40% of people find it acceptable for FRT to be used for tracking who enters or leaves residential buildings, monitoring employee attendance (30%), or seeing how people respond to advertising displays in real-time (15%).[2]
  • 56% of Americans believe that law enforcement will appropriately utilize face recognition technology.[2]
  • For tracking, monitoring, or reducing COVID-19 transmission, more than 40% of nations utilize face recognition.[2]
  • The 3D sector led the facial recognition market in 2020, making up 36% of the global revenue share, followed by the retail and e-commerce segment at 21%.[2]
  • From 2021 to 2028, the face recognition market, which was valued at $3.8 billion in 2020, is anticipated to expand at a compound annual growth rate of 15.4% .[2]
  • One indicative set of algorithms tested under the FRVT had an average miss rate of 4.7% on photos “from the wild” when matching without any confidence threshold.[3]
  • Leading algorithms exhibited accuracy ranges between 36% and 87% when detecting people passing through a sports event.[3]
  • The top algorithm had an accuracy rate of 94.4% when utilizing video of people going via boarding gates in a reasonably controlled environment.[3]
  • In around 30% of situations, the algorithm correctly recognized the person, but did so with less than 99% confidence, and as a result, claimed that it had no matches.[3]
  • Using the best practices as tools now download complex architectural systems can estimate an object’s face in a picture with 95% accuracy, outperforming humans’ 94% accuracy, according to einfochips.com.[4]
  • The market for computer-based vision has grown considerably. It is currently valued at $11.94 billion and is likely to reach $17.38 billion by 2023, at a CAGR of 7.80% between 2018 and 2023.[4]
  • By 2021, facial recognition will be in use at the top 20 U.S. airports for 100% of international passengers, including American citizens.[5]
  • 42.6% of respondents support using face recognition technology to increase boarding efficiency and security, according to reservations.com.[5]
  • Only one in three Americans (32.5%) disagree with the government using facial recognition technology at airports to improve security and boarding speed, according to a new survey from travel search engine Reservations.com.[5]
  • According to a report released in June 2019, the worldwide face recognition industry will produce $7 billion in sales by 2024, with a CAGR of 1.6% from 2019 to 2024.[6]
  • Academia in comparison to human face recognition scores of 97.53%, the 2014 Gaussian face algorithm created by academics at the Chinese University of Hong Kong attained facial identification ratings of 98.52%.[6]
  • According to a recent NIST study, significant improvements in recognition accuracy have been accomplished over the last five years, surpassing those made between 2010 and 2013.[6]
  • According to the Boston Globe, on June 27, 2019, the Somerville City council in Massachusetts’s decided to outlaw face recognition technology, becoming the second city to do so.[6]
  • According to Yahoo, the face recognition payment system will be implemented in 3,000 businesses by the end of the year.[6]

Also Read

How Useful is Image Recognition

One of the most touted benefits of image recognition is its ability to streamline processes and improve efficiency. In industries such as retail, image recognition is being used to optimize inventory management, making it easier for companies to track their products and forecast demand. Additionally, in healthcare, this technology is being leveraged to assist doctors in diagnosing illnesses and identifying abnormalities in medical images with greater accuracy and speed. These applications demonstrate the tangible benefits that image recognition can offer in increasing productivity and improving outcomes.

Moreover, image recognition has also had a significant impact on security and law enforcement. Facial recognition technology, in particular, has revolutionized surveillance and monitoring systems, enabling authorities to identify and track individuals in real-time. While this has been helpful in locating missing persons or apprehending suspects, concerns have also been raised about privacy violations and potential misuse of this technology for mass surveillance. As such, it is crucial for companies and government agencies to implement robust safeguards and regulations to ensure that image recognition is used responsibly and ethically.

On the consumer front, image recognition has transformed the way we interact with technology. Voice-activated virtual assistants, such as Amazon’s Alexa or Apple’s Siri, leverage image recognition algorithms to understand context and respond to user commands more effectively. Additionally, advancements in augmented reality technology have made it possible for consumers to try on clothes virtually or visualize furniture in their homes before making a purchase. These interactive experiences have enhanced the shopping experience for consumers, making it more immersive and personalized.

Despite its many advantages, image recognition technology is not without its limitations. The accuracy of these systems can vary depending on factors such as lighting conditions, image quality, and diversity of image datasets. Biases may also be present in these algorithms, reflecting societal prejudices and leading to potential discrimination in the identification of certain demographics. As a result, there is a need for continual refinement and testing of these systems to ensure that they are fair and reliable in their applications.

In conclusion, image recognition technology has proven to be extremely useful in a variety of industries and applications, offering numerous benefits in efficiency, security, and consumer experiences. However, it is imperative that we approach the adoption of this technology with caution and sensitivity to ethical concerns surrounding privacy and bias. By striking a balance between innovation and responsibility, we can harness the full potential of image recognition technology while safeguarding the rights and welfare of individuals in our digital world.

Reference

  1. financesonline – https://financesonline.com/facial-recognition-statistics/
  2. passport-photo – https://passport-photo.online/blog/facial-recognition-statistics/
  3. csis – https://www.csis.org/blogs/technology-policy-blog/how-accurate-are-facial-recognition-systems-%E2%80%93-and-why-does-it-matter
  4. einfochips – https://www.einfochips.com/blog/understanding-image-recognition-and-its-uses/
  5. reservations – https://www.reservations.com/blog/resources/facial-recognition-airports-survey/
  6. thalesgroup – https://www.thalesgroup.com/en/markets/digital-identity-and-security/government/biometrics/facial-recognition

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