Guides DPIA for biometric processing systems including facial recognition, fingerprint, voice, iris, and gait analysis. Covers Art. 9 special category requirements, Art. 35(3)(b) mandatory DPIA triggers for large-scale biometric processing, and EDPB Guidelines 3/2019 on video surveillance. Keywords: biometric, facial recognition, fingerprint, DPIA, Art. 9, special category, EDPB Guidelines 3/2019.
Biometric data is classified as a special category of personal data under GDPR Art. 9(1) when processed for the purpose of uniquely identifying a natural person. Processing biometric data on a large scale triggers a mandatory DPIA under Art. 35(3)(b). This skill provides a comprehensive DPIA methodology for biometric systems including facial recognition, fingerprint identification, voice recognition, iris scanning, vein pattern analysis, and behavioural biometrics (gait, typing patterns, signature dynamics).
"'Biometric data' means personal data resulting from specific technical processing relating to the physical, physiological or behavioural characteristics of a natural person, which allow or confirm the unique identification of that natural person, such as facial images or dactyloscopic data."
Processing of biometric data for the purpose of uniquely identifying a natural person is prohibited unless one of the Art. 9(2) exemptions applies.
Critical distinction: Art. 9 only applies when biometric data is processed "for the purpose of uniquely identifying" a person. A photograph used for illustration purposes is not Art. 9 data; the same photograph processed through facial recognition software to identify the person is Art. 9 data.
| Exemption | Reference | Application to Biometrics |
|---|---|---|
| Explicit consent | Art. 9(2)(a) | Employee consent often not freely given due to power imbalance (WP29 Opinion 2/2017). Consumer biometric consent must meet Art. 7 standards. |
| Employment, social security, social protection law | Art. 9(2)(b) | Member State law may authorise biometric processing in the employment context (e.g., biometric access control for high-security areas). |
| Vital interests | Art. 9(2)(c) | Limited to emergency situations where biometric identification is needed to protect someone's life. |
| Substantial public interest | Art. 9(2)(g) | Member State law basis required. May apply to law enforcement biometrics where authorised by specific legislation. |
| Health or social care | Art. 9(2)(h) | Biometric patient identification in healthcare settings. |
| Public health | Art. 9(2)(i) | Biometric contact tracing during health emergencies (subject to proportionality). |
| Archiving, scientific research, statistics | Art. 9(2)(j) | Biometric research (e.g., medical imaging analysis) with Art. 89(1) safeguards. |
Processing on a large scale of special categories of data referred to in Art. 9(1), including biometric data processed for unique identification, requires a DPIA. "Large scale" factors per WP248rev.01:
Key provisions relevant to facial recognition CCTV:
One-to-one comparison of a live biometric sample against a stored template for the claimed identity. Used for access control, device unlock, payment authentication.
| Risk Factor | Assessment |
|---|---|
| Data subjects | Defined, enrolled individuals |
| Volume of data | Limited to enrolled population |
| Proportionality | Generally more proportionate than identification |
| Storage recommendation | Template stored on user's device or card (decentralised) |
| Art. 35(3)(b) trigger | Depends on scale of enrolled population |
One-to-many comparison of a live biometric sample against a database of templates to determine identity. Used for law enforcement, border control, surveillance.
| Risk Factor | Assessment |
|---|---|
| Data subjects | Potentially unlimited (all persons in the capture area) |
| Volume of data | Can be very large (entire population databases) |
| Proportionality | Highly intrusive; requires strong justification |
| Storage recommendation | Centralised database is typically required for 1:N matching |
| Art. 35(3)(b) trigger | Almost always triggered |
Classification of individuals into groups based on biometric characteristics (age, gender, ethnicity, emotion) without uniquely identifying them. Used for analytics, targeted advertising, audience measurement.
| Risk Factor | Assessment |
|---|---|
| Art. 9 applicability | May not fall under Art. 9 if not used for unique identification, but still high risk |
| AI Act classification | Emotion recognition in workplace/education prohibited (Art. 5(1)(f)) |
| Discrimination risk | Categorisation by race, ethnicity, or emotion raises equality law concerns |
For biometric systems, the Art. 35(7)(a) systematic description must include:
| Element | Required Detail |
|---|---|
| Biometric modality | Facial, fingerprint, iris, voice, vein, gait, or multi-modal |
| Processing mode | Verification (1:1) or identification (1:N) or categorisation |
| Capture environment | Controlled (sensor/scanner) or uncontrolled (CCTV, ambient camera) |
| Template storage | Centralised database, decentralised (user device/card), or encrypted enclave |
| Template format | Proprietary template, ISO 19794 standard, or raw biometric data |
| Matching algorithm | Vendor algorithm (specify), open-source algorithm, or custom development |
| Accuracy metrics | False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER) |
| Liveness detection | Anti-spoofing measures (presentation attack detection) |
| Fallback mechanism | Alternative identification method when biometric fails |
| Retention period | Template retention, raw biometric data retention, audit log retention |
The proportionality assessment for biometric systems must be rigorous because biometric data:
| Proportionality Question | Assessment Standard |
|---|---|
| Is biometric processing necessary, or can a non-biometric alternative achieve the same purpose? | Badge/card access, PIN, password, or multi-factor authentication without biometrics |
| Is the biometric modality the least intrusive option? | Fingerprint is generally less intrusive than facial recognition; on-device verification less intrusive than centralised identification |
| Is the scale of biometric processing proportionate? | Processing all persons in an area (identification) is less proportionate than processing enrolled volunteers (verification) |
| Is the retention of biometric data minimised? | On-card template storage preferred over centralised database; raw biometric images should not be retained after template extraction |
| Risk ID | Risk | Likelihood | Severity | Typical Level |
|---|---|---|---|---|
| BIO-R1 | Biometric data breach — templates or raw data exposed to unauthorised parties | Possible | Maximum | Very High |
| BIO-R2 | Function creep — biometric data collected for access control repurposed for surveillance or attendance monitoring | Likely | Significant | High |
| BIO-R3 | Discriminatory accuracy — facial recognition performs worse on certain demographic groups (skin colour, age, gender) | Likely | Significant | High |
| BIO-R4 | False rejection denying legitimate access — disabled individuals, elderly, or those with skin conditions experience higher rejection rates | Possible | Significant | High |
| BIO-R5 | Spoofing or presentation attacks — fraudulent biometric samples (photos, masks, artificial fingerprints) bypass security | Possible | Significant | High |
| BIO-R6 | Chilling effect — knowledge of biometric surveillance alters behaviour in public or workplace spaces | Likely | Limited | High |
| BIO-R7 | Irreversibility — unlike passwords, compromised biometric data cannot be changed or reissued | Almost certain | Maximum | Very High |
| BIO-R8 | Third-party capture — biometric data of non-enrolled individuals incidentally captured by the system | Likely | Limited | High |
| Measure | Type | Risk Addressed |
|---|---|---|
| On-device or on-card template storage (no centralised database) | Technical (DPbD) | BIO-R1, BIO-R7 |
| Template protection: cancellable biometrics or biometric encryption (BioHashing, fuzzy vault) | Technical | BIO-R1, BIO-R7 |
| Liveness detection / presentation attack detection (ISO 30107 compliance) | Technical | BIO-R5 |
| Purpose limitation enforcement through technical access controls | Technical + Organisational | BIO-R2 |
| Demographic accuracy testing across skin colour, age, gender groups (NIST FRVT benchmarks) | Technical | BIO-R3 |
| Non-biometric fallback mechanism (PIN, card, helpdesk override) | Organisational | BIO-R4 |
| Signage and transparency notices in capture areas | Organisational (Transparency) | BIO-R6, BIO-R8 |
| Automatic deletion of raw biometric images after template extraction | Technical (Data minimisation) | BIO-R1 |
| Regular penetration testing of biometric system | Technical (Security) | BIO-R5 |