Behavioral biometrics is the collection and simultaneous analysis of unconscious behavior to accurately identify individuals. This includes how people walk, talk, hold mobile phones, look at things, gesture, and stand.
Most of the time, this data analyzes human behavior, but some scientists analyze animal behavior such as wing beats. They use the data to, for example, remotely species if not individual creatures.
The development of behavioral biometrics can be traced back to military defense programs like DARPA (Defense Advanced Research Projects Agency). However, most behavioral data comes from private security companies working with banking or financial institutions. These companies track this biometrical data through sensors, cameras, and technological devices based within the financial institution.
Due to the security applications, there are not many public databases for behavioral biometrics. Those that exist tend to provide data on animals or insects.
As noted above, behavioral biometrics can measure how a person types, holds a phone, walks, speaks, and stands. It may track their eye movement or the gestures they use.
However, the most important attribute of this data is the immediate, real-time collection and assessment of data. Naturally, to protect a client’s bank account, the biometrics security system must evaluate their typing and mobile phone holding behavior within the few minutes it may take for the intruder to log in to a client’s account and make a transaction.
Every institution, whether commercial, public, private, or any combination thereof, uses behavioral biometrics use cases to keep their data and assets secure. Other uses, such as insect species identification, remains secondary.
In addition to complete security of the data itself, a quality data set has a fair amount of accurate historical data on each individual it measures. Since some individuals infrequently visit the website or physical locations of whatever institution collects this data, there may not be enough information on them to ensure accuracy. Therefore, a good dataset should collect a representative sample of behavior on each client.
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