Biometric data are unique physical or behavioral characteristics that can be used to verify a person’s identity.
They do not change significantly with age and are unique for every individual. Common examples include fingerprints, iris patterns, facial features, and voice.
These technologies are already part of everyday life. For example, biometric passports contain a microchip that stores not only standard personal information but also the holder’s fingerprints.
Main Types of Biometrics
Fingerprints. One of the most common and reliable identification methods. Each person has a unique fingerprint pattern that can be used in forensics and for everyday authentication, such as unlocking a smartphone.
Facial recognition. Algorithms analyze the geometry of the face — shape, distance between the eyes, and lip contours — to create a digital template used for comparison. This is how Apple’s Face ID works.
Voice recognition. AI systems analyze tone, pitch, and rhythm of speech to identify speakers — for example, in transcription services or contact centers.
Iris recognition. The iris pattern is as unique as a fingerprint. Apple’s Vision Pro headset already uses this method to identify users by eye pattern.
Other biometric identifiers include palm vein patterns, gait, heartbeat (ECG), and even brainwave activity (EEG).
Regulation of Biometric Data
In the European Union, biometric data are regulated under the GDPR (General Data Protection Regulation).
Such data are classified as sensitive personal information, meaning that companies must obtain explicit user consent and ensure enhanced data protection.
If a business operates with EU citizens, these rules are mandatory regardless of location.
Where Biometrics Are Used
Authentication and access control. Unlocking a phone with a fingerprint is faster and more convenient than entering a password. The same principle applies in online banking, tax services, and digital government platforms.
Healthcare. Biometrics prevent patient identification errors. For instance, the Matcher 5 system is used in fertility clinics and donor banks to match patients via fingerprints.
Travel and security. The U.S. and Japan scan fingerprints at border control, while China uses facial recognition at customs to verify travelers’ identities.
Finance. Banks like Citibank have introduced face-based authentication in mobile apps to enhance client security.
Gaming industry. The game Nevermind measures players’ heart rate — the calmer the player, the scarier the story becomes.
Marketing. Research firm Nielsen tracks viewers’ eye movements, EEG, and heart rate to evaluate emotional responses to ads and improve campaign performance.
Industrial safety. The SmartCap system monitors workers’ fatigue levels using EEG sensors embedded in headbands. When attention drops, the system alerts the worker, reducing the risk of accidents.
Biometrics and Artificial Intelligence
For an AI system to recognize a face, voice, or emotion, it must be trained on large datasets containing annotated examples.
These labeled datasets include images, sound recordings, or videos where each element is precisely tagged.
For instance, when developing a model for facial and emotion recognition, our team collected thousands of human images and annotated them using 15 facial key points. This structured approach helps algorithms detect subtle patterns and improve recognition accuracy.
Google also applies biometrics in its Assistant: when a user speaks, their voice is recorded and analyzed by an ML model that splits the audio into signals, recognizes words, and becomes more accurate over time — adapting to accent, tone, and speech rate.
How Biometric Data Are Collected for Machine Learning
The collection method depends on the data type:
Images and videos — gathered through crowdsourcing, web scraping, or synthetic data generation.
Fingerprints — sourced from open databases or collected in voluntary research. For example, the company Papilon digitized national fingerprint archives in the 2000s, creating an automated identification system.
Voice recordings — collected via crowdsourcing platforms or from real contact center calls.
After collection, the data are annotated and converted into formats suitable for model training.
How Biometric Models Are Trained
Data collection. Images, audio, or video are gathered and labeled for the target task.
Training. Algorithms learn unique patterns — the digital “fingerprints” of identity.
Testing. The model’s accuracy is verified on new, unseen data.
Deployment. Once validated, the system is implemented in real environments.
A well-known example is facial recognition at Heathrow Airport (London).
Since 2019, passengers no longer need to show passports or boarding passes — cameras scan faces and verify them in advance, drastically reducing registration and security check times.
Biometrics have already become part of everyday life — from smartphones and banking to healthcare and industrial safety. Combined with machine learning, they make authentication more secure, processes faster, and technologies smarter. However, biometric data remain highly sensitive personal information, requiring careful handling, transparency, and protection.