Face recognition data uses machine learning algorithms to identify and recognize a human individual face through technology.
While any individual or private organization can develop these algorithms, most facial recognition data is stored by law enforcement agencies or other public services like the Department of Motor Vehicles, such as from CCTV cameras, traffic cameras, social media, or photos that police officers have taken themselves.
Facial recognition software identifies landmark features and the distances and angles between these features. Then the algorithm converts this information into a mathematical formula for storage. However, most attributes of face recognition data simply contain images of human faces. And many times, these images are marked with the nodal points that ML programs use to identify individuals.
Most uses of this data are law enforcement and security on both a private and public level. However, facial recognition technology shows up in a surprisingly large number of industries, including social media (such as when Facebook automatically tags people in photos) and healthcare (such as identifying rare genetic disorders).
Perhaps the most important aspect of facial recognition data quality is accuracy. Yet, this still-developing technology tends to issue both false positives and false negatives. Obviously, these types of issues can lead to large problems in security and law enforcement contexts.
Additionally, face recognition algorithms become less effective as the dataset increases in size. To resolve this, regularly cleanse the data of faces you no longer need to recognize.
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