Face recognition data uses machine learning algorithms to identify and recognize individual faces.
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.
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 (both private and public). 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, this can lead to massive 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 and consider training a human being to go through the database and confirm the face recognition program’s matches.
Researchers developed a new app that applies facial recognition software to cows. The technology would let ranchers track cattle in the event of disease and help create a national traceability system.
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