Facial recognition has evolved from a niche security tool into a common element of CCTV systems, retail analytics, access control, transport, and law enforcement operations. It is often presented as a technology that improves efficiency and safety, but it also involves serious privacy risks, numerous ethical dilemmas, and complex regulatory requirements. These threats increase when facial recognition is combined with broad surveillance infrastructure based on AI and behavioral analysis.
This article discusses the key risks related to facial recognition in video and CCTV systems, the applicable legal frameworks, and practical risk mitigation measures, including the role of anonymization and automatic face blurring as a less intrusive alternative.
Facial recognition as biometric data processing
To understand the scale of risk, it is necessary to first look at how the law classifies facial recognition. In most legal systems, this technology is treated as high-risk data processing because it concerns biological traits used for unique personal identification.
Biometric data and identifiability
Under GDPR, biometric data used for the purpose of uniquely identifying a person belongs to special categories of data, which means a higher level of protection and limited legal bases for processing [1]. The UK ICO emphasizes that facial recognition used in surveillance constitutes biometric processing and, as a rule, requires a demonstrated legal basis, necessity, and proportionality [2].
In the United States, some states, including Illinois, have adopted dedicated biometric laws such as BIPA, which impose strict requirements for consent, retention, and civil liability [3]. This means that deploying facial recognition without careful legal analysis can lead to serious financial and reputational consequences.
Combining facial recognition with continuous monitoring
Risks grow particularly when facial recognition is integrated with CCTV networks that conduct continuous background monitoring, often without real awareness on the part of observed individuals. The system shifts from passive event recording to active, large-scale identification of individuals in public and private spaces.
Accuracy, algorithmic bias, and misidentification
One of the best-documented threats concerns algorithmic inaccuracy and vulnerability to bias, especially in real-world, diverse environments.
Error rates and bias
Research conducted by NIST under the Face Recognition Vendor Test (FRVT) has indicated significant differences in false matches and errors across algorithms. Errors more often affected women and people with darker skin tones [4]. Similar conclusions come from research projects such as Gender Shades [5].
These disparities make facial recognition particularly problematic in critical contexts, such as law enforcement, access control systems, or public transport.
Technical and environmental factors
CCTV footage is often constrained by multiple limitations: poor lighting, fast movement, unusual camera angle, low resolution, and image compression. All of these factors reduce algorithmic accuracy and increase the risk of misidentification, especially in large, distributed surveillance networks.
Consequences of misidentification
The consequences of errors are not purely technical. Misidentification can lead to wrongful detentions, denial of services, workplace discrimination, placement on suspect lists, or restricted access to certain spaces. Documented incidents show that a single algorithmic error can have years-long consequences for an individual’s life [4][5].
Mass surveillance and loss of anonymity in public space
From passive monitoring to active identification
Traditional CCTV systems mainly recorded footage for later review after an incident. Facial recognition introduces an active component – near real-time identification, creation of “persons of interest” lists, and linking presence data with other databases.
The European Data Protection Board has repeatedly stated that biometric identification of people in public space is, as a rule, exceptionally difficult to reconcile with GDPR and rarely meets the requirements of necessity and proportionality [6].
Chilling effect and social profiling
Continuous identification in public space can lead to a chilling effect – discouraging participation in protests, political events, or visits to sensitive places such as healthcare facilities or religious institutions. Human rights organizations warn that facial recognition can influence behavior and lead to self-censorship.
Linking with other data sources
Facial recognition data can be combined with loyalty programs, geolocation data, law enforcement databases, mobile app data, or commercial data brokers. This can create a particularly intrusive profiling ecosystem in which the line between monitoring and tracking becomes very thin.
Legal and compliance risks
Organizations that use facial recognition expose themselves to significant legal, financial, and regulatory risks.
GDPR and biometric regulations
GDPR treats facial recognition as processing of special categories of data, which requires meeting additional conditions such as explicit consent or specific public interest grounds [1]. The EDPB indicates that biometric identification in public space is generally impermissible, except in narrowly defined exceptions [6].
US state laws
In the United States, laws such as Illinois BIPA impose obligations to obtain written consent, define retention periods, and publish detailed policies. Importantly, these laws allow civil lawsuits, which creates substantial liability risk [3].
Penalties and supervisory enforcement
In Europe, regulators have repeatedly imposed fines for the use of facial recognition without an adequate legal basis, without transparency, and without a data protection impact assessment. This shows that biometrics is treated as a high-risk area requiring particular caution.
Security risks and the impact of biometric data breaches
Biometrics as an attractive attack target
Biometric data is uniquely sensitive because, unlike a password or card number, it cannot be changed after a breach. Face templates can be used for impersonation, deepfake generation, or bypassing other biometric systems. The impact of a leak is long-term.
Weak links in CCTV and IoT infrastructure
Many CCTV systems run on outdated hardware, unpatched software, and weak network security. Adding a facial recognition module increases the attack surface, often requiring cloud or edge processing, which must also be properly secured.
Systemic risk in integrated networks
Integrated surveillance networks covering buildings, cities, transport, and retail can lead to large-scale breaches if one component becomes the weakest link. Potential breach impact grows as systems become more interconnected.
Ethical and social risks
Discrimination and unequal treatment
Biased algorithms make more errors for certain groups, especially ethnic minorities and women. NIST and MIT Media Lab (Gender Shades) studies showed significant accuracy differences across demographic groups [4][5]. This can lead to systemic discrimination.
Lack of transparency and notice
In many deployments, recorded individuals are not aware that facial recognition is being used. Clear information is often missing about where data is processed, who processes it, how long it is stored, and who may receive it. This lack of transparency undermines public trust.
Loss of trust in institutions
Hidden or poorly communicated facial recognition deployments can trigger reputational crises. Organizations perceived as overusing surveillance technology risk losing the trust of customers, citizens, or employees.
Risk mitigation strategies and safer alternatives
Anonymization and automatic face blurring
If identifying a specific person is not necessary, a much safer approach is to apply anonymization or automatic blurring of faces and license plates. Modern tools such as Gallio PRO automatically detect faces and visual identifiers, then apply advanced anonymization techniques, preserving material usability while protecting privacy.
Limiting facial recognition to absolutely necessary situations
Regulators recommend applying necessity and proportionality tests, as well as assessing less privacy-intrusive alternatives [2][6]. Facial recognition should not be the default option, but a last resort where other methods are insufficient.
Data Protection Impact Assessments (DPIA)
Conducting a Data Protection Impact Assessment is, in many cases, not only best practice but a legal requirement. DPIA helps identify risks, define technical and organizational safeguards, and document the decision to implement or reject a biometric solution.
Organizations that want to preserve video system functionality while reducing risk increasingly choose image anonymization. To see how this works in practice, it is worth downloading a free Gallio PRO demo and testing automatic face blurring in your own environment.
FAQ – common questions about facial recognition risks
Is facial recognition legal in all countries?
No. Many jurisdictions have restrictions, sector-specific rules, or even bans on facial recognition in public spaces. Local regulations should always be analyzed.
Why is facial recognition considered high-risk processing?
Because it involves biometrics – unique physical traits that can be used for unambiguous identification – and enables large-scale tracking of people over time. Errors or misuse have especially serious consequences.
Can misidentification lead to legal problems?
Yes. A false match can result in violations of personal rights, discrimination, wrongful detention, and, as a consequence, lawsuits and penalties from data protection authorities.
Is automatic face blurring a safer alternative?
In many scenarios, yes. When there is no need to identify individuals, anonymization or strong face blurring significantly reduces legal and ethical risk while preserving monitoring functionality.
Are AI facial recognition systems becoming fully objective?
No. Although the technical quality of many systems is improving, bias and performance gaps between groups are still being documented. Algorithms learn from historical data, which may already contain bias.
Is GDPR alone sufficient as a basis for deploying facial recognition?
No. GDPR defines the framework, but specific legal conditions for processing must be met, a DPIA must be performed, transparency must be ensured, and national regulations and supervisory guidance must be taken into account.
Bibliography
[1] Regulation (EU) 2016/679 (GDPR).
[2] UK ICO – Guidance on biometric recognition and surveillance.
[3] Illinois Biometric Information Privacy Act (BIPA).
[4] NIST – Face Recognition Vendor Test (FRVT).
[5] MIT Media Lab – Gender Shades.
[6] European Data Protection Board (EDPB) – guidelines on facial recognition in public spaces.