Top 5 Facial Recognition Challenges & Solutions
Facial recognition is now part of everyday life, from unlocking phones to verifying identities in public spaces. Its reach continues to grow, bringing both convenience and new possibilities. However, this expansion also raises concerns about accuracy, privacy, and fairness that need careful attention.
Discover the top 5 facial recognition challenges and solutions to prevent fraud and misuse:
Challenge | Best Practices |
|---|---|
Privacy & surveillance | Establish clear legal limits on use. Require consent in non-public settings. |
Bias & misidentification | Train on diverse datasets. Use independent bias testing. |
Data security & misuse | Encrypt all biometric data. Restrict access to authorized staff. |
Technical limitations | Apply 3D or generative models to handle occlusions. Combine facial recognition with other biometrics. |
Ethical & societal issues | Create independent ethics review boards. Educate the public about risks and safeguards. |
1. Privacy and surveillance
Facial recognition can be used to monitor people without their consent. When authorities or companies apply it in public areas, individuals may be identified and followed without realizing it. This kind of surveillance raises serious privacy concerns and can threaten civil liberties.
For example, the Metropolitan Police has expanded its deployment of live facial recognition in public spaces, but the scale of scanning varies by operation and is not continuously applied across the entire city.1
How to increase privacy?
- Establish clear legal frameworks to regulate government use and prevent unauthorized surveillance.
- Require written consent before collecting facial recognition data in non-public contexts.
- Implement transparency measures, such as audits and regular reporting on deployments.
- Limit storage of biometric data to specific identification purposes and strengthen data protection controls.
Real-life example: Street-level facial recognition
Federal immigration agents are increasingly using facial recognition technology during street operations, raising concerns about expanded government surveillance.
ICE and other Department of Homeland Security officials have used a smartphone app called Mobile Fortify to photograph and scan people’s faces in cities including Minneapolis, Chicago, and Portland, Maine. The app can compare images against government databases in real time and may store photos for up to 15 years, according to documents obtained through a Freedom of Information Act request. Witnesses say scans have included bystanders and U.S. citizens, not just enforcement targets.
DHS says the tool is lawful and helps identify persons of interest. But civil liberties groups and some lawmakers argue that street-level facial recognition may violate constitutional protections and normalize biometric surveillance in public spaces. Lawsuits and proposed legislation seek to curb the practice, as critics warn it could erode privacy and limit public activity.2
Real-life example: Meta’s Name Tag
Meta plans to bring facial recognition technology to its Ray-Ban smart glasses. The feature, internally called “Name Tag,” would allow users to identify people they see and access information about them through Meta’s AI assistant.
Prior to this development, Facebook shut down its facial recognition system in 2021, citing privacy and legal risks. After selling more than 7 million smart glasses in 2025 and facing growing competition in AI wearables, Meta sees facial recognition as a way to make its devices more useful and stand out in the market.
Internal discussions show the company is aware of the privacy and safety concerns. Meta has considered limiting the feature to recognizing people connected to a user on its platforms or those with public profiles, rather than offering open-ended identification.
Privacy advocates warn that putting facial recognition into consumer glasses could erode anonymity in public spaces and invite misuse.
At the same time, Meta argues the technology could improve accessibility, particularly for people who are blind or visually impaired. The company is also developing more advanced glasses designed to continuously capture visual data, with facial recognition powering reminders and contextual assistance.3
2. Bias and misidentification
While many facial recognition systems still show higher error rates for marginalized groups, top-tier models evaluated in recent NIST assessments4 have significantly reduced demographic accuracy gaps. Bias remains a concern, particularly in older or poorly curated systems.
To reduce bias and misidentification:
- Train models on diverse datasets representing multiple demographics.
- Require independent testing to identify algorithmic bias.
- Apply conservative thresholds and ensure human oversight of all matches.
- Prohibit law enforcement agencies from relying solely on automated outputs.
Real-life example: Racial representation in facial recognition
A computer science professor from the University at Buffalo’s Department of Computer Science and Engineering, Ifeoma Nwogu, explains that many algorithms achieve high accuracy only within narrowly representative training datasets, typically dominated by images of white males aged 18–35, which leads to significantly higher error rates for women and people of color.
Studies by Gender Shades and NIST have confirmed particularly low accuracy for Black women, illustrating how unbalanced data and camera technologies not optimized for darker skin tones reinforce systemic disparities.
Although recent advances in datasets, camera quality, and machine learning have improved accuracy, Nwogu emphasizes that meaningful oversight must occur at governmental and policy-making levels, as many societal harms stem from unintended consequences of deployed systems.
She argues that comprehensive regulation, increased technical literacy among policymakers, and continued research into diversity-aware models are essential to ensuring facial recognition is used responsibly and ethically.5
3. Data security and misuse
Facial data is especially sensitive because, unlike a password, it can’t be reset once exposed. If someone gains access to it, they could use it for identity theft, fraud, or unauthorized tracking. When these systems operate with little oversight, the chance of abuse only grows.
Support data security and minimize misuse by:
- Encrypting all stored facial recognition data and limiting retention periods.
- Mandating compliance with strong data protection standards and regular audits.
- Applying strict access controls to ensure only authorized personnel handle biometric data.
- Requiring clear incident response plans to protect individuals in the event of breaches.
Real-life example: Clearview AI privacy violations
Clearview AI is a U.S. company that provides facial recognition software built on a database of tens of billions of images scraped from publicly accessible websites. Law enforcement and government agencies upload a photo to the system, which returns possible matches and links to where those images appeared online. The technology has been used in criminal investigations and marketed to border and intelligence agencies.
The company has faced sustained legal and regulatory scrutiny over privacy concerns. Critics argue that Clearview collects and indexes facial images without individuals’ knowledge or consent. In the United States, it has been sued under biometric privacy laws, including Illinois’ Biometric Information Privacy Act, resulting in a major settlement. Courts in California have also allowed privacy claims over its database practices to proceed.
European regulators have repeatedly found Clearview in violation of data protection laws. Authorities in Greece and the Netherlands imposed multimillion-euro fines, citing unlawful collection of biometric data under the GDPR. Privacy groups have also pursued complaints seeking further legal action.
More recently, U.S. Customs and Border Protection signed a contract giving intelligence units access to Clearview’s system for tactical targeting, raising concerns about expanding biometric surveillance in routine government operations.6
4. Technical limitations in real-world conditions
Facial recognition tends to be less accurate in real-world conditions. Low light, masks, glasses, and changes in angle can all confuse the system, leading to errors. These issues make it harder to rely on the technology for identity checks, security access, or policing.
To increase real-world accuracy:
- Improve image-capture standards to ensure high-resolution inputs.
- Apply liveness detection to confirm that real people are present during scans.
- Use advanced methods such as 3D face modeling and GANs to reconstruct occluded features.
- Employ multimodal authentication (combining face with iris, fingerprint, or voice recognition) in sensitive areas.
Recently, researchers have increasingly used diffusion-based models and transformer architectures to reconstruct occluded facial features, as these methods outperform traditional GANs in terms of stability and accuracy.
Real-life example: Liveness detection with Yoti MyFace
Yoti MyFace Liveness is a passive liveness detection system that checks whether a selfie is being captured from a real, physically present person in real time, rather than from a spoof, such as a printed photo, replayed video, mask, or AI-generated deepfake.
It works by analyzing a single selfie using multiple neural network models to assess image quality and facial depth cues, and returning a confidence score within seconds. Unlike facial recognition, it does not identify who someone is; it only verifies that the face is live and genuine. It can also be configured to detect injection attacks in which a fake image or video is injected into the camera stream instead of a real capture.7
Real-life examples: Increasing facial recognition real-world effectiveness
According to a recent study, facial recognition systems continue to face significant challenges when used in real-world conditions. To address these limitations, researchers are developing methods such as deep learning, 3D facial modeling, and generative techniques that can reconstruct missing features.
The study highlights the benefits of combining facial recognition with other biometric approaches to enhance accuracy. It also emphasizes the importance of privacy-preserving techniques, such as federated learning and encryption.
It concludes that, despite rapid progress, challenges surrounding fairness, accuracy, and privacy must be addressed to ensure the responsible use of facial recognition technology.
Figure 1: The image shows 30 different types of common distortions and appearance changes.8
Another study on facial recognition challenges shows that surveillance and reconnaissance systems often suffer from reduced accuracy due to low-quality footage, occlusions (e.g., glasses), and demographic biases in training datasets.
To address these issues, the researchers developed a deep learning framework that uses autoencoders and generative adversarial networks (GANs) to generate synthetic data, manipulate facial attributes, and enhance degraded images.
Key components of this approach include a model to adjust skin tones for greater demographic representation, a system to remove eyeglasses while preserving identity, and an image enhancement module that improves clarity in low-resolution surveillance footage.
Tested on the CelebA dataset, the method demonstrated improved dataset diversity, reduced bias, and enhanced recognition accuracy in challenging conditions.9
5. Ethical and societal issues
The growing use of facial recognition has sparked serious ethical questions about fairness, openness, and public trust. When the technology is used without clear consent, it often faces strong public criticism. If its spread continues without proper limits, it could make constant surveillance seem normal and weaken fundamental rights.
Support ethical standards by:
- Mandating disclosure by businesses and government agencies on how facial recognition systems are used.
- Requiring meaningful opt-in consent for individuals.
- Creating independent ethical review boards to oversee deployments.
- Launching public awareness campaigns explaining both the benefits and risks of the technology.
Real-life example: Student attendance check with facial recognition
A recent report on India’s plan to use AI-based facial recognition for student attendance under the Students Achievement Tracking System (SATS) has raised major privacy and ethical concerns. Experts warn that collecting and storing children’s facial data could lead to misuse, including potential leaks to commercial actors or criminals.
They stress that schools should remain safe learning spaces, not sites of surveillance. Instead, they suggest improving School Development and Monitoring Committees (SDMCs) and adopting open-source tools as safer, more transparent options.10
The steps of facial recognition technology
A typical face recognition system follows a clear sequence:
- Image capture: The system records a facial image or frame from a video. The quality of facial scans significantly impacts the results, with high-resolution images typically yielding more accurate matches.
- Face detection: Specialized algorithms locate the face in the captured image and separate it from the background. This step is essential before analyzing facial features.
- Feature extraction: The system encodes unique facial features into a numerical template that represents a person’s identity. Some facial recognition technologies use three-dimensional data to enhance accuracy.
- Comparison: The extracted template is compared against stored facial recognition data in a database or against one specific face image, depending on whether the task is identification or verification.
- Decision: The system evaluates the level of similarity between the probe and stored data, then outputs potential matches or confirms an identity.
For example, Amazon Rekognition uses collections to store face vectors, which are mathematical representations of facial features rather than images.
The workflow is:
- Create a collection to hold facial data.
- Index faces to detect and store face vectors.
- Create a user and associate faces to group multiple images of the same person into a user vector for higher accuracy.
You can then search faces in images, stored videos, or streaming video using operations like SearchFacesByImage or SearchUsersByImage. This enables use cases such as authenticating employees at entry points by comparing live facial scans with stored data using similarity scores.11
How to measure recognition accuracy
Accuracy in facial recognition technology is measured through specific metrics that capture the likelihood of correct or incorrect matches. Common measures include:
- False Match Rate (FMR): The probability that the system incorrectly matches two different people.
- False Non-Match Rate (FNMR): The probability that the system fails to match two images of the same person.
- Identification rates: Metrics such as the rank-1 identification rate indicate how often the system correctly identifies individuals from an extensive database.
- Error trade-offs: Performance is often presented in graphs, such as ROC curves, which show how false positives and false negatives change as the decision threshold is adjusted.
Accuracy depends on the quality of the face images, lighting, angle, and even changes in appearance, such as facial hair. It also varies across facial recognition models, which raises important ethical concerns about algorithmic bias and fairness toward specific groups.
What is the confidence score in facial recognition?
A confidence score shows how certain a facial recognition system is that two faces belong to the same person. It measures similarity, not the exact chance of being correct. While a higher score means a closer match, the final judgment depends on the threshold defined within the system.
- Calibration: Confidence scores vary across facial recognition software and should be aligned with operational goals.
- Thresholds: In many jurisdictions, law enforcement systems generate candidate lists based on high-confidence thresholds, and officers are required to validate potential matches manually rather than relying on automated outputs
- Influence of conditions: Poor lighting, occlusion, or changes in unique facial features, such as new facial hair, can reduce confidence scores and affect outcomes.
- Policy implications: Because facial recognition data is sensitive biometric data, confidence thresholds must be managed with data protection safeguards, personal privacy considerations, and awareness of ethical issues such as racial bias and potential misuse in unauthorized surveillance.
Confidence scores, therefore, help balance the technology’s ability to identify individuals against the risks of false positives and the broader challenges of facial recognition that many businesses, government agencies, and law enforcement face.
FAQ
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
Be the first to comment
Your email address will not be published. All fields are required.