Facial Recognition Technology: 8 Check for Government Purchasing

The governments across the world are rapidly using facial recognition technology in different sectors such as law enforcement, airport safety, welfare distribution, and e-government services. Several agencies execute these technologies without clear risk estimation or accuracy evaluations. 

For example, the U.S. Government Accountability Office (GAO) revealed in 2021 that agencies such as, FBI and DEA had utilized the FRT without having a formal tracking system. The GAO report also showed that these agencies had overlooked the accuracy testing or privacy influence assessments. This absence of due diligence revealed the operational gaps and showed how important red flags are often ignored. 

FRT isn’t merely a modernization process—it’s a high-stakes technology with far-reaching implications for civil rights and social inclusion. After deployment, abuse is hard to undo if careful vetting is not done. That’s why governments need to implement higher procurement standards, making sure solutions pass vetted tests in bias reduction, liveness detection, data privacy, and security compliance before mass deployment.

Bias and Demographic Gaps in Facial Recognition

Facial recognition technology is not as neutral as one might hope. However, studies have revealed that such systems usually create confusion among individuals from definite racial and gender groups. 

For example, Joy Buolamwini and Timnit Gebru’s study revealed that commercial facial recognition solutions had some error rates up to 34.7% for dusky brown women, in contrast to less than 1% for fair-skinned men. This difference mainly comes from the fact that the datasets for training these algorithms consist mostly of lighter-skinned male faces, which results in biased outcomes. 

Takeaways for Governments

States that have ethnically and racially diverse populations or sectors, such as high demographic diversity such as airports, should reinforce that FRT vendors use comprehensive, representative datasets. Without this, some users from certain demographics often experience high rejection rates that lead to exclusion, wrongful arrest, or public criticism. The government must correctly estimate vendors based on their ability to provide constant performance across different racial and ethnic profiles. 

Privacy Issues in Biometric Data Collection

As FRT accumulates the highly sensitive biometric data, governments should not overlook where this data goes and for how long it can be kept. The absence of transparent retention policies, the threat of misuse or illegal access is enhanced, particularly when data is kept regularly. This issue has come up in scenarios such as the London Metropolitan Police, where the lack of transparency around data stored and usage has raised public criticism. 

FRT is now utilized not only for identity verification but also for age estimation, making it critical that vendors follow privacy regulations such as GDPR. Governments need to question whether important data is stored on premises or in the cloud and make sure it’s deleted when no longer required. 

Whereas certificates such as iBeta PAD Level 2 are valuable, industries should do more than self-evaluation. The best indicator of facial recognition software is how it performs in actual use cases. To test this, industries need to check the vendor’s current customers and see if the solution worked effectively in comparable cases.

Why Real-World Testing Is Essential for Accuracy

Facial recognition technology tends to be highly accurate in ideal lab conditions. These results do not always translate well to real-world conditions where variables such as lighting, crowd rates, and camera angles can affect performance significantly. For example, it has been demonstrated through studies that variations in ambient conditions or facial expressions will severely degrade the accuracy of the system. 

Real-World Accuracy Requires Environmental Testing

Additionally, the National Institute of Standards and Technology (NIST) has pointed out that facial recognition algorithms work best under controlled conditions in verification tasks but lose accuracy when used in identification tasks in real-world, uncontrolled environments. The disparity is a manifestation of the need to subject the system rigorously to the targeted operating environment before deployment. Sectors demanding FRT to function in varied situations, like border crossings, should ensure that the selected vendor can prove identities correctly under light-changing conditions, different camera angles, and crowd density. This assists in preventing misidentifications and secures public trust. 

The Role of Cybersecurity in Protecting Biometric Data

Facial recognition systems depend on static biometric information once disclosed, it can’t be changed or restored, so any safety breach is irreparable. The vendors’ faulty cybersecurity procedures can disclose data breaches, insider threats, or illegal monitoring risk, impacting public security and trust directly. 

The government must promise that vendors align with strong safety measurements before purchasing. These involve encoding, safe access controls, incident response strategies, and secure data storage, whether it’s a local server or cloud forum that is compliant. Third-party ISO/IEC 27001 certifications and periodic audits also establish the system’s immunity to emerging cyber threats.

Balancing FAR and FRR in Government Services

High false rejection rate (FRR) and false acceptance rate (FAR) can severely impact access to important government services. These mistakes are not just numbers, but they can result in legal access or allow it to impostors, particularly important in government use cases. 

If the system mistakenly refuses a real user (high FRR), they can be restricted from accessing important services, for instance, healthcare, social safety, or unemployment declare. The high FAR, on the other hand, can allow illegal individuals to manipulate the system, causing identity fraud. 

Such risks arise in remote identity verification cases, where facial recognition is usually the only way to confirm identity. What may seem like a technical problem has an intense social influence, especially when dealing with diverse facial aspects.

Reinforcing the accurate balance of FAR and FRR is now important to maintain safe and inclusive access across e-govt services that depend on authentication via facial biometrics. 

Detecting Spoofing Through Liveness and PAD Tools 

FRT can conveniently be tricked if it lacks robust security mechanisms against the latest spoofing attacks. These include advanced methods such as presentation attacks (e.g., printed images, video replays, or masks) and injection attacks (e.g., deepfakes), which pose serious security threats when the system fails to detect whether the face presented is real or fake.

If FRT is being used for identity verification and authentication purposes for access to government services, they must reinforce that vendors can offer layer protection. To safeguard against such threats, the following layers of protection are essential:

• Presentation Attack Detection (PAD):
  Must be integrated to confirm that a live, genuine person is being authenticated, not a spoofed face. Vendors certified with iBeta Level 2 PAD should be prioritized.
• Injection Attack Protection:
  Systems should be built to detect and block deepfake or synthetic media injected during the authentication process.
• 3D Liveness Detection:
  Uses analysis of micro-movements, blink patterns, depth cues, and other biometric signals to verify the presence of a live individual and thwart advanced spoofing attacks.

Without these technologies, facial recognition remains vulnerable to fraud, identity theft, and large-scale breaches.

These protections are critical when FRT is used for high-risk services such as:
ID issuance, border control, access to welfare benefits, sensitive health data, financial services, and online age verification.

Ensuring Vendor Transparency and Ethical Compliance

After estimating the major technical and ethical features of using facial recognition technology, it’s fairly necessary to consider solution providers’ transparency and reliability. A lack of clarity can expose governments to reputational damage, legal scrutiny, and gaps in service delivery. To prevent this, public institutions should take the following steps:

• Request extensive documentation from vendors covering system architecture, accuracy rates, data retention policies, and potential bias handling.
• Seek third-party performance approval through certifications or independent audits to validate system claims.
• Conduct systemized threat assessments to uncover hidden operational risks and ensure the solution aligns with public accountability standards.
• Avoid rushed procurement by ensuring vendors provide timely and complete performance disclosures before finalizing any agreement. 

A Responsible Roadmap for Government FRT Adoption

With governments driving digital inclusion and seeking to simplify access to core services such as healthcare, education, and financial support, the need for safe authentication through facial recognition has never been greater.

Why Facia is the Ideal Partner for Government Projects:

• Intrinsic Presentation Attack Detection (PAD) and deepfake attacks detection provide safeguards against sophisticated spoofing attacks.
• Proven across a wide range of demographic groups to reduce bias and provide equitable performance.
• Meets top security and privacy requirements, minimizing legal and reputational risk.
• Conceived with high accuracy in operational environments to facilitate trustworthy service delivery and uninterrupted user experience.