Bio-metric recognition and new patterns of behavioral data collection

Since some time on we have been hearing about bio-metric recognition and this technology has already been adopted in some airports to speed up transit procedures for passengers. In fact Bio-metric data are personal data because allow identity verification.

Bio-metric devices are actually very popular and are widespread in smartphones to enable the Face Unlock
Bio-metric characteristics can be biological but also behavioral and can lead to identification by performing the comparison one by one.
For example, fingerprints are bio-metric trait and allow identification in the comparison one by one and allow exclusion by making the comparison one by many.
Another example of bio-metric trait is the voice. The voice is the result of physical and anatomical characteristics that determine its qualities.
The most widely treated bio-metric trait is surely the face and requires necessarily the acquisition of an image. There are a varied selection of bio-metric sensors and different methods of data acquisition, however the purpose is common and it is to the elaboration of a mathematical representation (bio-metric model).

What are the risks associated with bio-metric recognition?

Social control, discriminatory use of data and bio-metric falsification should be considered critical and controversial matter, especially when such data are associated with the provision of a service.
Ethnicity or race, physical shape or health may, for example, be subject to discriminatory treatment, or may simply lead to categorization which may be considered discriminatory.
Using bio-metric recognition systems therefore requires the adoption of specific security measures that can ensure the temporary storage of data for the declared purposes and the awareness of the processing by the individual concerned.
For permission marketing purposes, this awareness can be assured by informing about the processing, but when marketing strategies are applied to unknown or unaware subjects it is important to adopt behavioral and visual data collection strategies that are absolutely compatible with the GDPR legislation.

Among these techniques are computer graphics and computer vision which, through the collection of color samples or temporary behavioral information, are able to develop a non-bio-metric mathematical model. This model, therefore, in a one-to-one comparison does not allow to be traced back to the individual when it is outside the specific context of the data collection or when it presents a minimal variation of the data.
That is why we talk about aggregated data that are specifically dependent on environmental circumstances. To give a concrete example, a subject who enters a store with red T-shirt and blue jeans is considered a subject, if the same subject is intercepted after a few minutes with a white T-shirt and blue jeans the computer vision will count a different and new one person.
This model of data acquisition and analysis of behavioral data is certainly more useful and more suitable for marketing purposes and can give the possibility to measure and understand your target audience. The data collection and its organized representation is becoming the main tool to make adaptive any strategy applied to the real world (shops, public spaces and places of transit).
The analysis of behavioral data in the real world becomes timely and always updated exactly as in the digital context.
The result is a huge potential for innovation, ranging from pure marketing to security to pure infotainment.

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Bio-metric recognition and new patterns of behavioral data collection

What are the risks associated with bio-metric recognition?

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