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Why Facial Recognition is The Best Biometric

By Hoan Ton-That BLOG

Identification is the bedrock of societal organization and cooperation. In the early days of small-scale societies, everyone knew each other, making it easy to know who bad actors in the community were. However, with urbanization, population growth, and the advent of globalization, this intimate knowledge of individuals becomes impossible, making it crucial to find effective ways to aid in identifying individuals, particularly those who pose threats to societal order and harmony.

Biometrics, or the identification of individuals based on their physical or behavioral characteristics, has long been used for identification. Fingerprints, with their unique patterns, have historically been the favored biometric identifier. However, with the advancements in technology, facial recognition has rapidly emerged as a superior alternative. This essay will explore why facial recognition technology is the best type of biometric, outperforming iris recognition and even the historically prevalent fingerprinting.


The ancient history of fingerprints is intertwined with the development of early societies, serving not only as a means of identification but also as a catalyst for fostering trust and enabling trade across borders. Notably, the Spanish explorer Joao de Barros documented that early Chinese merchants employed fingerprints to seal and authenticate business transactions, establishing credibility and accountability in their dealings. Additionally, the 14th-century Persian book "Jaamehol-Tawarikh" attests to the use of fingerprints for individual identification. This antiquated use of fingerprints extends back to 500 B.C., as evinced by the Babylonian clay tablets recording business transactions marked with fingerprints. These historical instances demonstrate the long-standing recognition of fingerprints as a unique, personal signature across diverse cultures and civilizations.

In more recent history, the Federal Bureau of Investigations (FBI) under J. Edgar Hoover popularized the use of fingerprints and championed their scientific accuracy for their use in criminal investigations.


Facial recognition has seen impressive progress in its accuracy and scalability over the last decade. While facial recognition technology started as a novel experiment on social media platforms like Facebook for photo tagging, it has become a critical tool in law enforcement, border control, and commercial settings. With the advent of deep learning and neural networks, the performance of facial recognition systems improved dramatically, achieving unprecedented accuracy levels. The popularity of the iPhone X, with its Face ID feature that launched in 2017, caused mass adoption and acceptance of facial recognition in consumer products.

Clearview AI has taken it a step further, achieving one-in-a-billion search accuracy in 2019, and currently over 30 billion images in our database of publicly available images.

Facial Recognition History

Recent Timeline

Facial Recognition History Timeline


The advantages of facial recognition over other biometrics lie in its non-contact, non-intrusive nature, and scalability. Unlike fingerprints or iris scans, facial recognition can be used at a distance, which means it has a wider set of possible use cases.

Moreover, facial recognition leverages existing infrastructure, such as CCTV cameras and online photographs, making it more scalable and cost-effective than other biometric systems. Most notably, the wealth of publicly available facial images available on social media and other online platforms significantly contributes to improving the accuracy and robustness of facial recognition systems.

Clearview AI for example is able to search billions of publicly available facial images from the internet. There is no comparable large source of fingerprint data or iris data available.


While iris recognition boasts high accuracy, it requires close and cooperative engagement from the individual, making it impractical for many applications. The technology is also sensitive to occlusions, such as glasses or contact lenses, and the need for custom expensive hardware limits their widespread use.


On the other hand, fingerprints have been a reliable method of identification for centuries. However, fingerprints can be easily smudged or worn out, reducing their reliability. They are also more susceptible to forgery and require direct contact with the scanner, raising hygiene concerns and limiting their application in non-cooperative or remote scenarios. They also require custom fingerprint reader hardware, while facial recognition can easily be used from a desktop computer or smartphone.


While DNA analysis offers an extremely accurate method of identification, its practical application is significantly limited compared to facial recognition, particularly when considering cost-effectiveness and hardware requirements. DNA analysis involves an intricate process of collecting, preserving, and analyzing biological samples, necessitating specialized laboratory equipment and skilled personnel. This process can be both time-consuming and expensive, making it prohibitive for routine or large-scale use.

On the other hand, facial recognition technology is remarkably more cost-effective and hardware agnostic. With the dramatic adoption of CCTV cameras and smartphones, there is a very high chance that any crime being committed will be caught on camera, thereby making facial recognition more useful. Furthermore, it takes only seconds to do a facial recognition search out of billions of images with Clearview AI.


Comparatively analyzing facial recognition and Social Security Numbers (SSNs) as means of identification reveals stark contrasts, particularly regarding security. SSNs, originally designed for tracking individual earnings and benefits, have been widely adopted as a de facto identifier for various purposes. However, the wide-ranging use and sharing of SSNs have inadvertently created a massive security risk. Countless data breaches and leaks over the years have exposed millions of SSNs, rendering them increasingly ineffective and insecure as a standalone identifier. Moreover, the proliferation of synthetic identity fraud, where criminals create new identities using a combination of real and fabricated information, often centered around a stolen SSN, further underlines the vulnerability of relying on SSNs for identification.

On the other hand, facial recognition leverages a person's unique physical characteristics for uniqueness. A criminal actor can create a new fake identity with a fake SSN, but they can not change their face, preventing future crime.

Facial Recognition is better than Fingerprints and Iris Recognition due to more information & robustness to variation.


While every biometric identification system has its pros and cons, facial recognition stands out due to its non-intrusive nature, scalability, and adaptability to various environments and applications. Advances in deep learning and the ubiquity of facial data have propelled facial recognition to unprecedented accuracy levels, making it a vastly superior alternative to both iris and fingerprint recognition due to (1) the amount of smartphone cameras and CCTV cameras that are ubiquitous, and (2) large scale databases of facial images, such as Clearview AI, which do not exist for fingerprints or iris scans and (3) not needing any custom hardware for deployments.

However, it is crucial to ensure that its use respects privacy rights and is regulated to prevent misuse. As we continue to innovate and improve upon this technology, facial recognition promises to revolutionize the way we identify individuals and ensure societal safety and order for centuries to come.



Founder & CEO of Clearview AI

A self taught engineer, Hoan Ton-That is of Vietnamese and Australian heritage. His father's family descended from the Royal Family of Vietnam. As a student, Hoan was ranked #1 solo competitor in Australia’s Informatics Olympiad. He was ranked #2 guitarist under age 16 in Australia’s National Eisteddfod Music Competition. At the age of 19, Hoan moved from Australia to San Francisco to focus on his career in technology. He created over twenty iPhone and Facebook applications with over 10 million installations, some of which ranked in the App Store’s Top 10. Hoan moved to New York City in 2016. In 2017, he co-founded Clearview AI and focused his energy on developing the core technology, raising capital, and building the team and product.

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