Choosing a Biometric Testing Laboratory: A Practical Guide for Vendors and End Users

Independence and Impartiality Are Non-Negotiable

ISO/IEC 17025 requires laboratories to operate impartially, and reputable schemes enforce separation between testing, consulting, and product development. Buyers should confirm that the laboratory does not also develop, resell, or hold a commercial stake in the technology under test.  

A practical conflict-of-interest check is whether the laboratory has helped build, tune, train, or supply datasets for the same product or vendor it is later being asked to test. For example, a laboratory that produces or curates training or benchmark datasets for a vendor may have a perceived or actual conflict if it subsequently evaluates that vendor’s product against related performance claims. Buyers should ask the laboratory to disclose any prior involvement with the technology, datasets, configuration, or vendor development process before relying on the report for procurement or certification evidence. 

This matters for vendors because reports provided by laboratories seen as partial are commonly challenged or rejected by regulators and downstream customers, and it matters for end users because an impartial evaluation is the only kind that delivers meaningful assurance. 

Report Quality Determines Whether Evidence Survives Scrutiny

A biometric test report should be readable by a technical reviewer up to two years after issue, without the authors present, and still answer the substantive questions a procurement reviewer, regulator, or audit cycle would put to it: methodology, sample composition, environmental conditions, statistical treatment, and confidence intervals. Buyers should request a redacted sample report before engaging a laboratory. 

Specific features to look for in a high-quality test report include:  

• clear articulation of the test plan against the governing standard 

• explicit reporting of the metrics appropriate for the evaluation type, with relevant confidence intervals. Typical examples include APCER, BPCER and IAPAR for PAD and IAD evaluations, and FMR, FNMR, FTA and FTE for biometric performance evaluations. 

• traceability between raw data and reported figures (an auditable path from each headline metric back to the underlying comparison decisions that produced it) 

• explicit reporting of edge cases, outliers, and how they were handled in the analysis.  

• exclusions, constraints, and any protocol deviations.  

• High-quality test reports use precise, evidence-based summary language and provide complete and balanced reporting of methods, results, limitations, and supporting data. 

Demographic Reach Matters for Fairness and Realism

A demographic differential performance evaluation under ISO/IEC 19795-10, informed by the methodology described in NIST Interagency Report 8280, depends on test crews that meaningfully represent the demographic groups relevant to the technology under test’s deployment context. A laboratory with a narrow subject pool will likely not produce credible demographic differential performance results for a product deployed across multiple regions. End users particularly should ask how a laboratory recruits and manages subjects, how it documents demographic composition, and how it handles intersectional categories. 

Data Governance Is Part of the Service, Not an Afterthought

Biometric evaluations generate sensitive data: face images, fingerprint captures, liveness video, device telemetry, and often personally identifiable information from test subjects. A laboratory’s data handling procedures should be documented under ISO/IEC 27001, or an equivalent information security management framework, and include written policies on subjects such as data retention, access control, cross-border transfer, and subject consent. For programmes operating under regulatory regimes such as the Reserve Bank of India’s digital payment security and biometric data directions, Bank Negara Malaysia’s e-KYC and Risk Management in Technology (RMiT) requirements, the State Bank of Vietnam’s Circular 50/2024, or Australia’s Digital ID Act 2024 and AGDIS framework, the lab should be able to articulate how its data handling processes align. 

For Vendors: Prioritise Pathway Clarity and Iteration Support

A vendor preparing for testing is buying two things from a laboratory: a compliant report and the shortest credible path to that report. The laboratory should be able to map the vendor’s product to the relevant scheme, identify the most efficient testing pathway, and flag likely pre-test issues before a formal evaluation begins. Pre-assessment or readiness review services can surface attack surface weaknesses, and metric shortfalls before a formal evaluation begins, when remediation is typically cheaper than after. Vendors should also ask about the laboratory’s experience with their specific modality, capture environment, and deployment context, because generic biometric competence does not always translate across modalities or form factors. 

For End Users: Prioritise Assurance and Procurement Alignment

An end user procuring a biometric system is buying assurance that the system will perform as claimed, for the population it will serve, under the conditions of operational use. The relevant question is, “has the product been tested in a way that answers my risk questions?” For example, a FIDO BCC certification confirms component-level performance and PAD resistance, but does not by itself, answer operational questions about enrolment drop-off in a branch environment or demographic performance across a specific customer base. This is why end users should consider whether a technology evaluation alone meets their assurance needs, or whether scenario and operational evaluations under ISO/IEC 19795-2 and 19795-6 are also required.