Types of biometric data
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As already mentioned, different biometric data may be derived from different characteristics a natural person exhibits – physical physiological, behavioral. The present section illustrates these different types of biometric data. The following taxonomy is not established as a standard and certain types of biometric data might be categorized differently by different experts. For example, taxonomies sometimes cluster physiological biometric data into physical biometric data.

Physical biometric data

Physical biometric data can be generated by capturing distinctivebodily features of individuals. The distinctiveness of these features can then be employed as an identifier. Some of the most common physical biometric characteristics are fingerprints, hand shape, facial features (such as the roundness of the face, the distance between the eyes, etc.), and iris features.

Physiological biometric data

Physiological biometric data can be generated by observing bodily functionsand capturing distinctive patterns associated to them. Some of the most common physiological biometric data are generated from electrocardiograms (ECG), respiration patterns, and electroencephalograms (EEG).

Although physical biometric data is often used as a synonym of physiological biometric data – and vice versa – the authors believe a distinction could be beneficial to better frame the discussion, especially considering recent studies on the relation between biometric technology and certain physiological functions, such as neurophysiological ones[1].

Behavioral biometric data

Behavioral biometric data can be generated by observing the behavior of individuals, to identify distinctive patterns in such behavior. Some are inherent to the individuals – such as gait, or voice – while others require the interaction with specific tools to manifest– such as handwriting, keystroke dynamics, and mouse movement.

Differently from physical biometric data, behavioral biometric data require an observation of the individuals that introduces a time variable in the assessment[2]. It is often contended that, despite being more volatile to momentarily fluctuations and changes through the lifetime, behavioral biometric data have the advantages of being less intrusive and cost effective[3]. However, some scholars have observed that behavioral biometric data might introduce more privacy risks compared to other kinds of biometric data[4], due to their capability to reveal further information on data subjects, sometimes of very sensitive nature such as health condition[5].



1See for instance, Patrizio Campisi and Daria La Rocca, ‘Brain Waves for Automatic Biometric-Based User Recognition’, IEEE Transactions on Information Forensics and Security 9, no. 5 (May 2014): 782–800, https://doi.org/10.1109/TIFS.2014.2308640.

2See Roman V. Yampolskiy and Venu Govindaraju, ‘Behavioural Biometrics: A Survey and Classification’, International Journal of Biometrics 1, no. 1 (2008): 81, https://doi.org/10.1504/IJBM.2008.018665.

3See Madeena Sultana, Padma Polash Paul, and Marina Gavrilova, ‘A Concept of Social Behavioral Biometrics: Motivation, Current Developments, and Future Trends’, in 2014 International Conference on Cyberworlds (2014 International Conference on Cyberworlds (CW), Santander, Cantabria, Spain: IEEE, 2014), 271–78, https://doi.org/10.1109/CW.2014.44.

4See Günter Schumacher, ‘Behavioural Biometrics: Emerging Trends and Ethical Risks’, in Second Generation Biometrics: The Ethical, Legal and Social Context, ed. Emilio Mordini and Dimitros Tzovaras (Springer, 2012).

5See for instance Marcos Faundez-Zanuy et al., ‘Handwriting Biometrics: Applications and Future Trends in e-Security and e-Health’, Cognitive Computation 12, no. 5 (September 2020): 940–53, https://doi.org/10.1007/s12559-020-09755-z.

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